U.S. patent application number 15/460187 was filed with the patent office on 2017-06-29 for image reconstruction system and method.
This patent application is currently assigned to SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD.. The applicant listed for this patent is SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD.. Invention is credited to Hao CHEN, Kai CUI, Juan FENG, Yecheng HAN, Yange MA, Jie NIU, Ji QI, Wanli TENG, Le YANG, Na ZHANG, Haihua ZHOU.
Application Number | 20170186192 15/460187 |
Document ID | / |
Family ID | 59086038 |
Filed Date | 2017-06-29 |
United States Patent
Application |
20170186192 |
Kind Code |
A1 |
YANG; Le ; et al. |
June 29, 2017 |
IMAGE RECONSTRUCTION SYSTEM AND METHOD
Abstract
A method and system for image reconstruction are provided. A
projection image of a projection object may be obtained. A
processed projection image may be generated based on the projection
image through one or more pre-process operations. A reconstructed
image including an artifact may be reconstructed based on the
processed projection image. The artifact may be a detector edge
artifact, a projection object edge artifact, and a serrated
artifact. The detector edge artifact, the projection object edge
artifact, and the serrated artifact may be removed from the
reconstructed image.
Inventors: |
YANG; Le; (Shanghai, CN)
; ZHOU; Haihua; (Shanghai, CN) ; FENG; Juan;
(Shanghai, CN) ; CUI; Kai; (Shanghai, CN) ;
QI; Ji; (Shanghai, CN) ; ZHANG; Na; (Shanghai,
CN) ; CHEN; Hao; (Shanghai, CN) ; NIU;
Jie; (Shanghai, CN) ; HAN; Yecheng; (Shanghai,
CN) ; TENG; Wanli; (Shanghai, CN) ; MA;
Yange; (Shanghai, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD. |
Shanghai |
|
CN |
|
|
Assignee: |
SHANGHAI UNITED IMAGING HEALTHCARE
CO., LTD.
Shanghai
CN
|
Family ID: |
59086038 |
Appl. No.: |
15/460187 |
Filed: |
March 15, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15317382 |
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PCT/CN2016/099061 |
Sep 14, 2016 |
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15460187 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 11/003 20130101;
G06T 2207/10081 20130101; G06T 7/187 20170101; G06T 7/11 20170101;
G06T 2207/30068 20130101 |
International
Class: |
G06T 11/00 20060101
G06T011/00; G06T 7/11 20060101 G06T007/11 |
Foreign Application Data
Date |
Code |
Application Number |
Sep 15, 2015 |
CN |
201510583366.0 |
Sep 15, 2015 |
CN |
201510583397.6 |
Jan 29, 2016 |
CN |
201610066684.4 |
Claims
1. A method comprising: obtaining a projection image of a
projection object; pre-processing the projection image to generate
a processed projection image; reconstructing the processed
projection image to generate a reconstructed image including an
artifact; and removing the artifact in the reconstructed image,
wherein the artifact includes a detector edge artifact relating to
a detector edge, a projection object edge artifact relating to a
projection object edge, and a serrated artifact, and wherein
removing the artifact in the reconstructed image includes: removing
the detector edge artifact; removing the projection object edge
artifact; and removing the serrated artifact; the pre-processing
the projection image including segmenting the projection image to
generate a segmented projection image.
2. The method of claim 1, the pre-processing the projection image
further including: generating a negative film of the segmented
projection image; and correcting a geometrical error of the
negative film of the segmented projection image.
3. The method of claim 1, the reconstructing the processed
projection image to generate the reconstructed image including:
filtering the processed projection image to generate a filtered
projection image including a highlighted artifact and an X-ray
attenuation artifact; correcting the highlighted artifact and the
X-ray attenuation artifact in the filtered projection image to
generate a first image; and performing back projection to generate
the reconstructed image based on the first image.
4. (canceled)
5. The method of claim 1, the reconstructed image including a
tomographic image, and the removing the serrated artifact
including: determining a mapping position of the detector edge in
the tomographic image; determining the projection object edge in
the tomographic image; determining an intersection point
corresponding to the projection object edge and the mapping
position of the detector edge; determining dislocation information
of the intersection point based on the intersection point and the
serrated artifact; and removing the serrated artifact based on the
intersection point and the dislocation information of the
intersection point.
6. The method of claim 5, the determining a mapping position of the
detector edge in the tomographic image including: determining a
first geometric position relationship between a radiation source
and the detector; determining a second geometric position
relationship between the projection image and the tomographic
image; determining mapping coordinates of pixels in the projection
image based on the first geometric position relationship and the
second geometric position relationship; and determining the mapping
position of the detector edge based on the mapping coordinates of
pixels in the projection image and an imaging area of the detector
in projection image.
7. The method of claim 5, the dislocation information of the
intersection point is a horizontal distance between the
intersection point and a point on an edge of the serrated
artifact.
8. The method of claim 5, the removing the serrated artifact based
on the intersection point and the dislocation information of the
intersection point including: creating a projection object template
of the tomographic image; removing the serrated artifact in the
projection object template to obtain a corrected projection object
template; and removing the serrated artifact in the tomographic
image based on the corrected projection object template.
9. The method of claim 1, the segmenting the projection image to
generate a segmented projection image comprising: determining an
average gray value of one or more pixels of the projection image;
for each pixel of the one or more pixels of the projection image,
assigning mark A or mark B to the pixel based on a relationship
between a gray value of the pixel and the average gray value; and
determining a boundary of a region of interest based on the
assigned mark of each pixel of the one or more pixels of the
projection image.
10. The method of claim 9, the boundary of the region of interest
is determined based on a seed filing algorithm.
11. A non-transitory computer readable medium comprising executable
instructions that, when executed by at least one processor, cause
the at least one processor to effectuate a method comprising:
obtaining a projection image of a projection object; pre-processing
the projection image to generate a processed projection image;
reconstructing the processed projection image to generate a
reconstructed image including an artifact; and removing the
artifact in the reconstructed image, wherein the artifact includes
a detector edge artifact relating to a detector edge, a projection
object edge artifact relating to a projection object edge, and a
serrated artifact, and wherein removing the artifact in the
reconstructed image includes: removing the detector edge artifact;
removing the projection object edge artifact; and removing the
serrated artifact; the pre-processing the projection image
including segmenting the projection image to generate a segmented
projection image.
12. A system comprising: a pre-procession module configured to
pre-process a projection image to generate a processed projection
image, the pre-procession module including a segmentation unit
configured to generate a segmented projection image; a
reconstruction module configured to reconstruct the processed
projection image to generate a reconstructed image including an
artifact; and an artifact removal module configured to remove the
artifact, wherein the artifact includes a detector edge artifact
relating to a detector edge, a projection object edge artifact
relating to a projection object edge, and a serrated artifact.
13. The system of claim 12, the pre-procession module further
including a negative film unit configured to generate a negative
film of the segmented projection image; and a geometrical error
correction unit configured to correct a geometrical error of the
negative film of the segmented projection image.
14. The system of claim 12, the reconstruction module further
including a filtered projection image generation unit, an artifact
correction unit, and a back projection unit, the filtered
projection image generation unit configured to generate a filtered
projection image including a highlighted artifact and an X-ray
attenuation artifact; the artifact correction unit configured to
correct the highlighted artifact and the X-ray attenuation artifact
in the filtered projection image to generate a first image; and the
back projection unit configured to perform back projection to
generate the reconstructed image based on the first image.
15. The system of claim 14, the reconstructed image including a
tomographic image, the tomographic image including a serrated
artifact relating to the tomographic image, and the removing
serrated artifact relating to the tomographic image including:
determining a mapping position of the detector edge in the
tomographic image; determining the projection object edge in the
tomographic image; determining an intersection point corresponding
to the projection object edge and the mapping position of the
detector edge; determining dislocation information of the
intersection point based on the intersection point and the serrated
artifact; and removing the serrated artifact relating to the
tomographic image based on the intersection point and the
dislocation information of the intersection point.
16. The system of claim 15, the determining a mapping position of
the detector edge in the tomographic image including: determining a
first geometric position relationship between a radiation source
and the detector; determining a second geometric position
relationship between the projection image and the tomographic
image; determining mapping coordinates of pixels in the projection
image based on the first geometric position relationship and the
second geometric position relationship; and determining the mapping
position of the detector edge based on the mapping coordinates of
pixels in the projection image and an imaging area of the detector
in projection image.
17. The system of claim 15, wherein the dislocation information of
the intersection point is a horizontal distance between the
intersection point and a point on an edge of the serrated artifact
relating to the tomographic image.
18. The system of claim 15, the removing the serrated artifact
relating to the tomographic image based on the intersection point
and the dislocation information of the intersection point
including: creating a projection object template of the tomographic
image; removing the serrated artifact relating to the tomographic
image in the projection object template to obtain a corrected
projection object template; and removing the serrated artifact
relating to the tomographic image in the tomographic image based on
the corrected projection object template.
19. (canceled)
20. (canceled)
21. The system of claim 12, wherein generating the segmented
projection image comprises: determining an average gray value of
one or more pixels of the projection image; for each pixel of the
one or more pixels of the projection image, assigning a mark to the
pixel based on a relationship between a gray value of the pixel and
the average gray value; and determining a boundary of a region of
interest based on the assigned mark of each pixel of the one or
more pixels of the projection image.
22. The system of claim 21, wherein the boundary of the region of
interest is determined based on a seed filing algorithm.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority of Chinese Patent
Application No. 201510583366.0 filed on Sep. 15, 2015, Chinese
Patent Application No. 201510583397.6 filed on Sep. 15, 2015, and
Chinese Patent Application No. 201610066684.4 filed on Jan. 29,
2016, the entire contents of each of which are hereby incorporated
by reference.
TECHNICAL FIELD
[0002] The present disclosure generally relates to image
processing, and more particularly, to a system and method for image
reconstruction.
BACKGROUND
[0003] Imaging reconstruction techniques are widely used in disease
diagnosis. However, reconstructed images may include a variety of
artifacts, which may cause misdiagnose. Thus, it may be desirable
to develop an image reconstruction method and system that may
remove or reduce artifacts to improve the quality of reconstructed
image.
SUMMARY
[0004] The present disclosure relates to image processing. One
aspect of the present disclosure relates to a method for image
reconstruction. The method may include one or more of the following
operations. A projection image of a projection object may be
obtained. A processed projection image may be generated according
to one or more pre-processing operations on the projection image.
For example, the pre-processing the projection image may include
segmenting the projection image to generate a segmented projection
image. A reconstructed image including an artifact may be generated
based on the processed projection image. The artifact may be
removed in the reconstructed image.
[0005] In some embodiments, the pre-processing the projection image
may further include generating a negative film of the segmented
projection image, and correcting a geometrical error of the
negative film of the segmented projection image.
[0006] In some embodiments, the reconstructing the processed
projection image to generate the reconstructed image may include
filtering the processed projection image to generate a filtered
projection image including a highlighted artifact and an X-ray
attenuation artifact, correcting the highlighted artifact and the
X-ray attenuation artifact in the filtered projection image to
generate a first image, and performing back projection to generate
the reconstructed image based on the first image.
[0007] In some embodiments, the artifact may include a detector
edge artifact relating to a detector edge, a projection object edge
artifact relating to a projection object edge, and a serrated
artifact.
[0008] In some embodiments, the detector edge artifact, the
projection object edge artifact, and the serrated artifact may be
removed in the reconstructed image.
[0009] In some embodiments, the reconstructed image may include a
tomographic image.
[0010] In some embodiments, the removing serrated artifact in a
tomographic image may include one or more of the following
operations. A mapping position of the detector edge in the
tomographic image may be determined. A projection object edge in
the tomographic image may be determined. An intersection point
corresponding to the projection object edge and the mapping
position of the detector edge may be determined. Dislocation
information of the intersection point based on the intersection
point and the serrated artifact may be determined. The serrated
artifact may be removed based on the intersection point and the
dislocation information of the intersection point.
[0011] In some embodiments, the determining a mapping position of
the detector edge in the tomographic image may include one or more
of the following operations. A first geometric position
relationship between a radiation source and the detector may be
determined. A second geometric position relationship between the
projection image and the tomographic image may be determined.
Mapping coordinates of pixels in the projection image based on the
first geometric position relationship and the second geometric
position relationship may be determined. The mapping position of
the detector edge based on the mapping coordinates of pixels in the
projection image and an imaging area of the detector in projection
image may be determined.
[0012] In some embodiments, the dislocation information of the
intersection point is a horizontal distance between the
intersection point and a point on an edge of the serrated
artifact.
[0013] In some embodiments, the removing the serrated artifact
based on the intersection point and the dislocation information of
the intersection point may include one or more of the following
operations. A projection object template of the tomographic image
may be created. The serrated artifact may be removed in the
projection object template to obtain a corrected projection object
template. The serrated artifact may be removed in the tomographic
image based on the corrected projection object template.
[0014] In some embodiments, the segmenting the projection image to
generate a segmented projection image may include one or more of
the following operations. An average gray value of one or more
pixels of the projection image may be determined. For each pixel of
the one or more pixels of the projection image, mark A or mark B
may be assigned to the pixel based on a relationship between a gray
value of the pixel and the average gray value. A boundary of a
region of interest based on the assigned mark of each pixel of the
one or more pixels of the projection image may be determined.
[0015] In some embodiments, the boundary of the region of interest
may be determined based on a seed filing algorithm.
[0016] Another aspect of the present disclosure relates to a
non-transitory computer readable medium including executable
instructions. The instructions, when executed by at least one
processor, may cause the at least one processor to effectuate a
method for image reconstruction. In some embodiments, the
non-transitory computer readable medium may include instructions
for causing a computer to implement the method described
herein.
[0017] A further aspect of the present disclosure relates to a
system for image reconstruction. The system may include a
pre-procession module to pre-process a projection image to generate
a processed projection image. In some embodiments, the
pre-procession module may include a segmentation unit, a negative
film unit, and a geometrical error correction unit. The system may
further include a reconstruction module to reconstruct the
processed projection image to generate a reconstructed image
including an artifact. In some embodiments, the artifact may be a
detector edge artifact relating to a detector edge, a projection
object edge artifact relating to a projection object edge, and a
serrated artifact. The system may further include an artifact
removal module to remove the artifact.
[0018] Additional features will be set forth in part in the
description which follows, and in part will become apparent to
those skilled in the art upon examination of the following and the
accompanying drawings or may be learned by production or operation
of the examples. The features of the present disclosure may be
realized and attained by practice or use of various aspects of the
methodologies, instrumentalities and combinations set forth in the
detailed examples discussed below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The present disclosure is further described in terms of
exemplary embodiments. These exemplary embodiments are described in
detail with reference to the drawings. The drawings are not to
scale. These embodiments are non-limiting exemplary embodiments, in
which like reference numerals represent similar structures
throughout the several views of the drawings, and wherein:
[0020] FIG. 1 illustrates a schematic diagram of an image
reconstruction system 100 according to some embodiments of the
present disclosure;
[0021] FIG. 2A illustrates an exemplary imaging device according to
some embodiments of the present disclosure;
[0022] FIG. 2B illustrates an architecture of a computer which may
be used to implement a specialized system incorporating the present
teaching;
[0023] FIG. 3A illustrates an exemplary image processing device
according to some embodiments of the present disclosure;
[0024] FIG. 3B is a block diagram illustrating an exemplary
pre-procession module according to some embodiments of the present
disclosure;
[0025] FIG. 3C is a block diagram illustrating an exemplary
reconstruction module according to some embodiments of the present
disclosure;
[0026] FIG. 3D is a block diagram illustrating an exemplary
artifact removal module according to some embodiments of the
present disclosure;
[0027] FIG. 4 illustrates a flowchart illustrating an exemplary
process for image reconstruction in accordance with some
embodiments of the present disclosure;
[0028] FIG. 5 is a flowchart illustrating an exemplary process for
pre-processing projection image in accordance with some embodiments
of the present disclosure;
[0029] FIG. 6 is a flowchart illustrating an exemplary process for
segmenting projection image in accordance with some embodiments of
the present disclosure;
[0030] FIG. 7 is a flowchart illustrating an exemplary process for
determining the boundary of a region of interest in accordance with
some embodiments of the present disclosure;
[0031] FIG. 8 is a flowchart illustrating an exemplary process for
generating a reconstructed image in accordance with some
embodiments of the present disclosure;
[0032] FIG. 9 is a flowchart illustrating an exemplary process for
removing artifact in a reconstructed image in accordance with some
embodiments of the present disclosure;
[0033] FIG. 10 is a flowchart illustrating an exemplary process for
removing serrated artifact in accordance with some embodiments of
the present disclosure;
[0034] FIG. 11 is a flowchart illustrating an exemplary process for
removing serrated artifact in accordance with some embodiments of
the present disclosure;
[0035] FIG. 12 illustrates a reconstructed image of a mammary
gland;
[0036] FIG. 13 illustrates an exemplary reconstructed image of a
mammary gland;
[0037] FIG. 14 illustrates an exemplary reconstructed image of a
mammary gland without serrated artifact correction;
[0038] FIG. 15A and FIG. 15B illustrate exemplary reconstructed
images of a mammary gland with serrated artifacts;
[0039] FIG. 16A to FIG. 16D illustrate exemplary mammary gland
templates;
[0040] FIG. 17 illustrates exemplary mammary gland reconstructed
images;
[0041] FIG. 18 illustrates an exemplary projection image of a
mammary gland;
[0042] FIG. 19 illustrates a process for generating a segmented
region by merging a plurality of regions of interest according to
some embodiments of the present disclosure;
[0043] FIG. 20 illustrates a process for generating a segmented
region based on a rectangular segmenting algorithm according to
some embodiments of the present disclosure; and
[0044] FIG. 21 illustrates an exemplary reconstructed image of a
mammary gland.
DETAILED DESCRIPTION
[0045] In the following detailed description, numerous specific
details are set forth by way of examples in order to provide a
thorough understanding of the relevant disclosure. However, it
should be apparent to those skilled in the art that the present
disclosure may be practiced without such details. In other
instances, well known methods, procedures, systems, components,
and/or circuitry have been described at a relatively high-level,
without detail, in order to avoid unnecessarily obscuring aspects
of the present disclosure. Various modifications to the disclosed
embodiments will be readily apparent to those skilled in the art,
and the general principles defined herein may be applied to other
embodiments and applications without departing from the spirit and
scope of the present disclosure. Thus, the present disclosure is
not limited to the embodiments shown, but to be accorded the widest
scope consistent with the claims.
[0046] It will be understood that the term "system," "engine,"
"unit," "module," and/or "block" used herein are one method to
distinguish different components, elements, parts, section or
assembly of different level in ascending order. However, the terms
may be displaced by other expression if they may achieve the same
purpose.
[0047] It will be understood that when a unit, engine, module or
block is referred to as being "on," "connected to," or "coupled to"
another unit, engine, module, or block, it may be directly on,
connected or coupled to, or communicate with the other unit,
engine, module, or block, or an intervening unit, engine, module,
or block may be present, unless the context clearly indicates
otherwise. As used herein, the term "and/or" includes any and all
combinations of one or more of the associated listed items.
[0048] The terminology used herein is for the purposes of
describing particular examples and embodiments only, and is not
intended to be limiting. As used herein, the singular forms "a,"
"an," and "the" may be intended to include the plural forms as
well, unless the context clearly indicates otherwise. It will be
further understood that the terms "include" and/or "comprise," when
used in this disclosure, specify the presence of integers, devices,
behaviors, stated features, steps, elements, operations, and/or
components, but do not exclude the presence or addition of one or
more other integers, devices, behaviors, features, steps, elements,
operations, components, and/or groups thereof.
[0049] The present disclosure provided herein relates to an image
reconstruction system. Specifically, the present disclosure relates
to a system and method for reconstructing image. According to some
embodiments of the present disclosure, the method may include
pre-processing a projection image to generate a processed
projection image. The pre-processing a projection image may include
segmenting the projection image to generate a segmented projection
image. The processed projection image may be reconstructed to
generate a reconstructed image including an artifact. The method
may further include removing the artifact in the reconstructed
image. The removing the artifact in the reconstructed image
including removing a detector edge artifact, removing a projection
object edge artifact, and removing a serrated artifact.
[0050] FIG. 1 illustrates a schematic diagram of an image
reconstruction system 100 according to some embodiments of the
present disclosure. Image reconstruction system 100 may include an
imaging device 110, an image processing device 120, a terminal 130,
a display 140, a database 150, and a network 160. In some
embodiments, at least part of image processing device 120 may be
implemented on computer 200 shown in FIG. 2B.
[0051] Imaging device 110 may obtain an image. The image may be a
three-dimensional (3D) image, a two-dimensional (2D) image, or the
like, or a combination thereof. The image may be a projection
image, a re-projection image, or the like, or a combination
thereof. The image may be a digital breast tomosynthesis (DBT)
image, a full-field digital mammography system (FFDM) image, and a
magnetic resonance (MR) image, or the like, or a combination
thereof. The image may be an image of an object. For example, the
image may be a 3D projection image of a mammary gland. The image
may be a 2D projection image of a mammary gland. In some
embodiments, a 3D image may correspond to a stack of 2D images. A
2D image may be referred to as a tomographic image or a slice
image. For instance, a 3D digital image of a mammary gland may
correspond to a stack of 2D tomographic images of the mammary
gland.
[0052] Imaging device 110 may utilize various imaging techniques.
The imaging technique may be a non-invasive imaging technique or an
invasive imaging technique. The technique may be based on or relate
to radiography (e.g., fluoroscopy, projection radiography, etc.),
magnetic resonance imaging (MRI), nuclear medicine (e.g.,
scintigraphy, single-photon emission computerized tomography
(SPECT), positron emission tomography (PET), etc.), ultrasound
(e.g., ultrasound scanning (US), etc.), elastography (e.g.,
quasistatic elastography/strain imaging, shear wave elasticity
imaging (SWEI), acoustic radiation force impulse imaging (ARFI),
supersonic shear imaging (SSI), and transient elastography, etc.),
tactile imaging, photoacoustic imaging, thermography, tomography,
conventional tomography, computer-assisted tomography (e.g., X-ray
computed tomography (CT), positron emission tomography (PET),
magnetic resonance imaging (MRI), etc.), echocardiography,
functional near-infrared spectroscopy (FNIR), digital subtraction
angiography (DSA), computed tomography angiography (CTA), digital
radiation (DR), magnetic resonance angiography (MRA), or the like,
or a combination thereof.
[0053] In some embodiments, imaging device 110 may include an
X-radiation source and a radiation detector (not shown in FIG. 1).
Imaging device 110 may use a low dose X-ray to create a
three-dimensional image of the breast. For example, imaging device
110 may be a digital breast tomosynthesis (DBT) shown in FIG.
2A.
[0054] Image processing device 120 may process an image. For
example, image processing device 120 may reconstruct an image to
generate a reconstructed image, enhance an image to generate an
enhanced image, extract some information from an image, remove
artifact of an image, or the like, or a combination thereof. The
image may be obtained by imaging device 110 or retrieved from
another source (e.g., database 150, a storage, etc.). The
reconstructed image may include one or more tomographic images. For
example, image processing device 120 may reconstruct a 3D
tomographic image of a mammary gland based on one or more mammary
gland projection images obtained by imaging device 110.
[0055] Image processing device 120 may be any kind of device that
may process an image. For example, image processing device 120 may
include a high-performance computer specialized in image processing
or transaction processing, a personal computer, a portable device,
a server, a microprocessor, an integrated chip, a digital signal
processor (DSP), a pad, a PDA, or the like, or a combination
thereof. In some embodiments, imaging processing device 120 may be
implemented on computer 200 shown in FIG. 2B.
[0056] Image processing may include performing one or more
operations on the image. The operations may include image
manipulation (e.g., rotating, flipping, resizing, cropping, etc.),
image segmentation, image reconstruction, image filtering, image
binarization, image overlapping, image matching, image negative
film, image artifact removing, color correction, geometric
transformation, image noise reduction, image enhancement, image
compression, or the like, or a combination thereof. In some
embodiments, image processing device 120 may segment image to get a
region of interest, and perform image reconstruction operation on
the region of interest.
[0057] Methods used in image processing may include an image
reconstruction method, an image segmentation method, an image
binarization method, an image artifact removing method, or the
like, or a combine thereof. As used herein, "removing" artifact may
refer to completely or partially remove artifact that is present or
identified by an image processing technology or method.
[0058] Image reconstruction methods may include filtered back
projection (FBP), the simultaneous iterative reconstruction
technique (SIRT), matrix inversion tomosynthesis (MITS), iterative
maximum a posteriori statistical reconstruction, Bayesian-based
interactive reconstruction, or the like, or a combination thereof.
More descriptions regarding a filtered back projection may be found
elsewhere in the present disclosure. See, for example, FIG. 4 and
FIG. 8, and the description thereof.
[0059] Image segmentation methods may include an edge detecting
method, a threshold segmenting method, a histogram-based method, a
clustering method, a compression-based method, a region-growing
method, a graph partitioning method, or the like, or a combination
thereof. More details descriptions regarding region-growing method
may be found elsewhere in the present disclosure. See, for example,
FIG. 6 and FIG. 7, and the description thereof.
[0060] Image artifact removing methods may include a polynomial
interpolation method, an iterative deblurring method, an
expectation-maximization method, an algebraic reconstruction
technique, a Markov random field method, a wavelet method, an
ordered subsets convex iterative method, a beam-stop technique, a
scanning lead-strip technique, or the like, or a combination
thereof. More details descriptions regarding image artifact
removing methods may be found elsewhere in the present disclosure.
See, for example, FIG. 10 and FIG. 11, and the description
thereof.
[0061] Terminal 130 may be connected to or communicate with image
processing device 120 and allow one or more operators to control
the production and/or display of images on display 140. Terminal
130 may include an input device, an output device, a control panel
(not shown in figure), or the like, or a combination thereof. The
input device may be a keyboard, a touch screen, a mouse, a remote
controller, a wearable device, or the like, or a combination
thereof. An input device may include alphanumeric and other keys
that may be inputted via a keyboard, a touch screen (e.g., with
haptics or tactile feedback), a speech input, an eye tracking
input, a brain monitoring system, or any other comparable input
mechanism. The input information received through the input device
may be communicated to image processing device 120 via network 160
for further processing. Another type of the input device may
include a cursor control device, such as a mouse, a trackball, or
cursor direction keys to communicate direction information and
command selections to, for example, image processing device 120 and
to control cursor movement on display 140 or another display
device.
[0062] Display 140 may display information. The information may
include an image before and/or after image processing, a request
for input or parameter relating to image acquisition and/or
processing, or the like, or a combination thereof. The display
device may include a liquid crystal display (LCD), a light emitting
diode (LED)-based display, a flat panel display or curved screen
(or television), a cathode ray tube (CRT), or the like, or a
combination thereof.
[0063] Database 150 may store images and/or relevant information or
parameters. Exemplary parameters may include the coordinate of the
radiation source, the radiation angle of the radiation source, the
coordinate of the rotating center of the radiation source, the
pixel size of a projection image, the width of a projection image,
the height of a projection image, the coordinate vector of a pixel
in a projection image, the width of a reconstructed image, the
height of a reconstructed image, the pixel size of a reconstructed
image, the coordinate vector of a pixel in a reconstructed image,
or the like, or a combination thereof.
[0064] Network 160 may establish connection between different units
in system 100. Network 160 may be a single network, or a
combination of various networks. Network 160 may be a wired network
or a wireless network. The wired network may include using a Local
Area Network (LAN), a Wide Area Network (WAN), a Bluetooth, a
ZigBee, a Near Field Communication (NFC), or the like, or a
combination thereof. The wireless network may be a Bluetooth, a
Near Field Communication (NFC), a wireless local area network
(WLAN), WiFi, a Wireless Wide Area Network (WWAN), or the like, or
a combination thereof.
[0065] It should be noted that the descriptions above in relation
to image reconstruction system 100 is provided for the purposes of
illustration, and not intended to limit the scope of the present
disclosure. For persons having ordinary skills in the art, various
variations and modifications may be conducted under the guidance of
the present disclosure. However, those variations and modifications
do not depart the scope of the present disclosure. For example,
part or all of the image obtained by imaging device 110 may be
processed by terminal 130. In some embodiments, imaging device 110
may pre-process the obtained image, before the image data is sent
to the image processing device 120 for further processing. In some
embodiments, terminal 130 and display 140 may be combined with
image processing device 120 as single device. Similar modifications
should fall within the scope of the present disclosure.
[0066] FIG. 2A illustrates an exemplary imaging device 110
according to some embodiments of the present disclosure. Imaging
device 110 may obtain a projection image of a projection object.
The projection object may be an organism, and an organ (e.g., a
mammary gland, a hand, a head, a lung, etc.), or the like, or a
combination thereof. The projection image may be further processed
by imaging processing device 120 (shown in FIG. 1 and FIG. 3A) or
computer 200 (shown in FIG. 2B). Imaging device 110 may include a
radiation source 201, a detector 203, and a compression plate
202.
[0067] Radiation source 201 may emit radiation. The radiation may
be electromagnetic radiation (e.g., X-ray, gamma radiation, visible
light, etc.), particle radiation (e.g., alpha radiation, beta
radiation, neutron radiation, etc.), acoustic radiation (e.g.,
ultrasound), gravitational radiation, or the like, or a combination
thereof. In some embodiments, radiation source 201 may be an X-ray
source. In some embodiments, radiation source 201 may be configured
as a bulb that may emit X-radiation.
[0068] Radiation source 201 may include an even number (e.g., two,
four, eight, sixteen, thirty, etc.) of sub-sources, or an odd
number (e.g., one, three, five, thirty-one, etc.) of sub-sources.
As used herein, a sub-source (illustrated as 201-1 through 201-N in
FIG. 2A) of radiation source 201 may include a device or a
structural component that may emit radiation. For instance, a
sub-source may include a bulb that may emit radiation. In some
embodiments, the number of sub-sources of radiation source 201 may
be one. In some embodiments, the number of sub-sources of radiation
source 201 may be more than one. At least two of a plurality of
sub-sources of radiation sources 201 may be the same or different
in type (e.g., X-ray source, gamma radiation source, etc.). At
least two sub-sources of radiation source 201 may have the same or
different characteristic parameter(s) (e.g., volume, shape, power,
tube current, geometric magnification, total magnification, focus
port size, radiation protection, etc.).
[0069] Merely by way of example, radiation sub-sources 201-1
through 201-4 may provide X-ray radiation, and radiation sub-source
201-N may provide gamma radiation. In some embodiments, the power
of radiation sub-source 201-1 may be 3 W, and the power of
radiation sub-sources 201-2 through 201-N may be 5 W. A source to
image-receptor distance (SID) may be any length (e.g., 0.5 m, 0.8
m, 1.0 m, 1.5 m, etc.). As used herein, SID may refer to a distance
between radiation source 201 and a projection image-receptor (e.g.,
detector 203, etc.). If radiation source 201 includes a plurality
of sub-sources, SID may refer to a distance between a sub-source of
radiation source 201 and a projection image-receptor (e.g.,
detector 203, etc.). The SID of sub-sources 201-1 through 201-N may
be the same or different.
[0070] In some embodiments, radiation sub-sources 201-1 through
201-N may be arranged in a straight line. The distances between two
neighboring radiation sub-sources 201 may be the same or different.
In some embodiments, all of radiation sub-sources 201-1 through
201-N may be arranged in a same line and the distances between each
two neighboring radiation sub-sources (e.g., between radiation
sub-source 201-1 and radiation sub-source 201-2, between radiation
sub-source 201-2 and radiation sub-source 201-3, between radiation
sub-source 201-3 and radiation sub-source 201-4, etc.) may be the
same. In some embodiments, radiation sub-sources 201-1 through
201-N may be arranged in a curved line, and at least two arc
lengths between neighboring radiation sub-sources (e.g., between
radiation sub-source 201-1 and radiation sub-source 201-2,
radiation sub-source 201-2 and radiation sub-source 201-3, etc.)
are the same or different.
[0071] In some embodiments, radiation source 201 may be arranged in
a whole or a part of circle with projection object (e.g., a mammary
gland) at the center of the circle.
[0072] The location of one or more radiation sub-sources 201-1
through 201-N may be fixed or movable. In some embodiments, the
location of one or more radiation sub-sources 201-1 through 201-N
may be fixed as described above. In some embodiments, the location
of one or more radiation sub-sources 201-1 through 201-N may be
changed according to the configurations of image reconstruction
system 100. For example, radiation source 201 (or a radiation
sub-source) may revolve around a projection object to take one or
more projection images. Radiation source 201 (or a radiation
sub-source) may revolve around a projection object in any angle
range (e.g., -15.degree. to +15.degree., -25.degree. to
+25.degree., -40.degree. to +65.degree., -65.degree. to
+90.degree., etc.) when the vertical direction is denoted as
0.degree., a negative angle indicates an anti-clockwise rotation,
and a positive angle indicates a clockwise rotation. Radiation
source 201 (or a radiation sub-source) may emit radiation at any
fixed frequency of angle (e.g., in every 1.degree., in every
2.degree., in every 5.degree., and in every 10.degree., etc.). For
example, radiation source 201 (or a radiation sub-source) may emit
radiation at a fixed frequency of every 5.degree. with an angle
range -15.degree. to +15.degree. (i.e., at -15.degree.,
-10.degree., -5.degree., 0.degree., +5.degree., +10.degree.,
+15.degree.). Radiation source 201 (or a radiation sub-source) may
emit radiation at a variable frequency of angle. For example,
radiation source 201 (or a radiation sub-source) may emit radiation
in 1.degree., 4.degree., 10.degree., 30.degree., and
90.degree..
[0073] Merely by way of example, radiation source 201 (or a
radiation sub-source) may revolve around a projection object
between -15.degree. to +15.degree. and emit radiation at every
1.degree.. In that case, 31 projection images may be generated. As
another example, radiation source 201 (or a radiation sub-source)
may revolve around projection object between -25.degree. to
+25.degree. and emit radiation at every 2.degree.. In that case, 26
projection images may be generated.
[0074] Compression plate 202 and detector 203 and may hold the
projection object from two opposite (or essentially opposite)
directions. Compression plate 202 may be made of a rigid material.
Compression plate 202 may be flat or curved. In some embodiments,
compression plate 202 may be made of a material transparent to
radiation (e.g., X-ray, etc.). Compression plate 202 may be
parallel (or essentially parallel) to detector 203 (shown in FIG.
2A).
[0075] Detector 203 may measure the flux, spatial distribution,
spectrum, and/or other properties of radiations. Radiation emitted
by radiation source 201 may pass through a projection object and
reach detector 203 to generate a projection image on detector 203.
Detector 203 may be a direct semiconductor detector, a gas-filled
detector, a scintillation detector, or the like, or a combination
thereof. Detector 203 may have an energy resolution including, for
example, 125 eV, 145 eV, 165 eV, 180 eV, 190 eV, 205 eV, 225 eV,
etc. Detector 203 may have a detecting area of, for example, 6
mm.sup.2, 7 mm.sup.2, 13 mm.sup.2, 25 mm.sup.2, etc. Detector 203
may have a thickness of, for example, 200 .mu.m, 300 .mu.m, 450
.mu.m, 500 .mu.m, 700 .mu.m, etc. Detector 203 may have a peaking
time of, for example, 11.2 .mu.s, 32 .mu.s, 44.8 .mu.s, etc.
[0076] In some embodiments, a projection object may be a mammary
gland. A projection image may be a projection image of the mammary
gland. Radiation source 201 may be an X-ray source. Detector 203
may be an X-ray detector.
[0077] A projection image taken by imaging device 110 may be sent
to image processing device 120, data base 150, display 140, and/or
terminal 130 via network 160 shown in FIG. 1. In some embodiments,
the projection image taken by imaging device 110 may be sent to
image processing device 120. Image processing device 120 may
process the projection image. For example, image processing device
120 may generate a 3D reconstructed image based on a plurality of
projection images. In some embodiments, the projection image may be
a projection image of a mammary gland. Image processing device 120
may generate a 3D reconstructed image of a mammary gland based on a
plurality of projection images of the mammary gland. The 3D mammary
gland reconstructed image may include one or more tomographic
images of a mammary gland.
[0078] It should be noted that the descriptions above in relation
to imaging device 110 is provided for the purposes of illustration,
and not intended to limit the scope of the present disclosure. For
persons having ordinary skills in the art, various variations and
modifications may be conducted under the guidance of the present
disclosure. However, those variations and modifications do not
depart the scope of the present disclosure.
[0079] FIG. 2B illustrates an architecture of a computer 200 which
may be used to implement a specialized system incorporating the
present teaching. Such a specialized system incorporating the
present teaching has a functional block diagram illustration of a
hardware platform that includes user interface elements. Computer
200 may be a general purpose computer or a special purpose
computer. Both may be used to implement a specialized system for
the present teaching. Computer 200 may be used to implement any
component of image processing as described herein. For example,
image processing device 120, etc. may be implemented on a computer
such as computer 200, via its hardware, software program, firmware,
or a combination thereof. Although only one such computer is shown,
for convenience, the computer functions relating to image
processing as described herein may be implemented in a distributed
fashion on a number of similar platforms, to distribute the
processing load. In some embodiments, computer 200 may be used as
imaging processing device 120 shown in FIG. 1.
[0080] Computer 200, for example, may include COM ports 211
connected to and from a network connected thereto to facilitate
data communications. Computer 200 may also include a central
processing unit (CPU) 205, in the form of one or more processors,
for executing program instructions. The exemplary computer platform
may include an internal communication bus 204, program storage, and
data storage of different forms, e.g., disk 208, read only memory
(ROM) 206, or random access memory (RAM) 207, for various data
files to be processed and/or communicated by the computer, as well
as possibly program instructions to be executed by CPU 205.
Computer 200 may also include an I/O component 209, supporting
input/output flows between the computer and other components
therein such as user interface elements 213. Computer 200 may also
receive programming and data via network communications.
[0081] Hence, aspects of the methods of the image processing and/or
other processes, as described herein, may be embodied in
programming. Program aspects of the technology may be thought of as
"products" or "articles of manufacture" typically in the form of
executable code and/or associated data that is carried on or
embodied in a type of machine readable medium. Tangible
non-transitory "storage" type media include any or all of the
memory or other storage for the computers, processors, or the like,
or associated modules thereof, such as various semiconductor
memories, tape drives, disk drives and the like, which may provide
storage at any time for the software programming.
[0082] All or portions of the software may at times be communicated
through a network such as the Internet or various other
telecommunication networks. Such communications, for example, may
enable loading of the software from one computer or processor into
another, for example, from a management server or host computer of
a scheduling system into the hardware platform(s) of a computing
environment or other system implementing a computing environment or
similar functionalities in connection with image processing. Thus,
another type of media that may bear the software elements includes
optical, electrical and electromagnetic waves, such as used across
physical interfaces between local devices, through wired and
optical landline networks and over various air-links. The physical
elements that carry such waves, such as wired or wireless links,
optical links or the like, also may be considered as media bearing
the software. As used herein, unless restricted to tangible
"storage" media, terms such as computer or machine "readable
medium" refer to any medium that participates in providing
instructions to a processor for execution.
[0083] Hence, a machine-readable medium may take many forms,
including but not limited to, a tangible storage medium, a carrier
wave medium or physical transmission medium. Non-volatile storage
media include, for example, optical or magnetic disks, such as any
of the storage devices in any computer(s), or the like, which may
be used to implement the system or any of its components shown in
the drawings. Volatile storage media may include dynamic memory,
such as a main memory of such a computer platform. Tangible
transmission media may include coaxial cables; copper wire and
fiber optics, including the wires that form a bus within a computer
system. Carrier-wave transmission media may take the form of
electric or electromagnetic signals, or acoustic or light waves
such as those generated during radio frequency (RF) and infrared
(IR) data communications. Common forms of computer-readable media
may include, for example: a floppy disk, a flexible disk, hard
disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or
DVD-ROM, any other optical medium, punch cards paper tape, any
other physical storage medium with patterns of holes, a RAM, a PROM
and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a
carrier wave transporting data or instructions, cables or links
transporting such a carrier wave, or any other medium from which a
computer may read programming code and/or data. Many of these forms
of computer readable media may be involved in carrying one or more
sequences of one or more instructions to a physical processor for
execution.
[0084] Those skilled in the art will recognize that the present
teachings are amenable to a variety of modifications and/or
enhancements. For example, although the implementation of various
components described herein may be embodied in a hardware device,
it may also be implemented as a software only solution--e.g., an
installation on an existing server. In addition, image processing
as disclosed herein may be implemented as a firmware,
firmware/software combination, firmware/hardware combination, or a
hardware/firmware/software combination.
[0085] FIG. 3A illustrates an exemplary image processing device 120
according to some embodiments of the present disclosure. Image
processing device 120 may include an initialization module 310, a
pre-procession module 320, a reconstruction module 330, and an
artifact removal module 340. Components in image processing device
120 may be connected to or communicate with each other and other
components in image reconstruction system 100, for example, imaging
device 110, terminal 130, display 140, database 150, or the like,
or a combination thereof.
[0086] Initialization module 310 may initialize or adjust one or
more parameters relating to the configuration of image
reconstruction system 100. For example, the parameter(s) may be
related to imaging device 110, image processing device 120, and
terminal 130, or the like, or a combination thereof. The
parameter(s) may be obtained from imaging device 110, image
processing device 120, terminal 130, database 150, or the like, or
a combination thereof. The parameter(s) may be determined based on
data obtained from imaging device 110, image processing device 120,
terminal 130, database 150, or the like, or a combination
thereof.
[0087] The parameter(s) may include a coordinate of the radiation
source, a radiation angle of the radiation source, the coordinate
of a rotating center of the radiation source, the pixel size of a
projection image, the width of a projection image, the height of a
projection image, the coordinate vector of a pixel in a projection
image, or the like, or a combination thereof.
[0088] In some embodiments, the parameter(s) may be a physical
coordinate of image reconstruction system 100 (e.g., the coordinate
of the radiation source, a radiation angle of the radiation source,
and the coordinate of a rotating center of the radiation source,
etc.), and an image parameter of a projection image (e.g., the
pixel size of a projection image, the width of a projection image,
and the height of a projection image, etc.).
[0089] Pre-procession module 320 may pre-process images. The images
may be obtained by imaging device 110 or retrieved from another
source (e.g., a database 150, a storage, etc.). Pre-procession
module 320 may perform one or more pre-processing operations on the
image. The pre-processing operations may include, for example,
image segmentation, image negative film generation, geometrical
error removal, color correction, geometric transformation, image
noise reduction, image enhancement, image compression, or the like,
or a combination thereof.
[0090] As illustrated in FIG. 3B, pre-procession module 320 may
include a segmentation unit 321, a negative film unit 323, and a
geometrical error correction unit 325. Segmentation unit 321 may
segment a projection image to generate a segmented projection
image. Negative film unit 323 may generate a negative film of an
image (e.g., a segmented projection image, etc.). Geometrical error
correction unit 325 may correct a geometrical error of an image
(e.g., a negative film of a segmented projection image, etc.).
[0091] Image segmentation may be performed based on, for example,
an edge detecting method, a threshold segmenting method, a
histogram-based method, a clustering method, a compression-based
method, a region-growing method, a graph partitioning method, or
the like, or a combination thereof. The image segmentation
operation may be performed by segmentation unit 321. In some
embodiments, image segmentation may be performed based on a
region-growing method that may also be referred as a seed filling
method. More descriptions about seed filling method may be found
elsewhere in the present disclosure. See, for example, FIG. 6 and
FIG. 7, and the description thereof.
[0092] In some embodiments, a projection image may be a projection
image of a mammary gland (or referred to as a mammary gland
projection image). The mammary gland projection image may be
pre-processed by one or more pre-processing operations including,
for example, image segmentation, image negative film generation,
geometrical error removal, or the like, or a combination
thereof.
[0093] Pre-procession module 320 may generate a processed image.
The processed image may be sent to one or more other components in
image processing device 120, for example, reconstruction module
330, artifact removal module 340, or the like, or a combination
thereof. The processed image may be sent to one or more components
in reconstruction system 100, for example, terminal 130, display
140, database 150, or the like, or a combination thereof.
[0094] In some embodiments, pre-procession module 320 may
pre-process a mammary gland projection image. The projection image
may be obtained by imaging device 110 or retrieved from another
source (e.g., a database 150, a storage, etc.). Pre-procession
module 320 may generate a processed mammary gland projection image.
The processed mammary gland projection image may be send to, for
example, reconstruction module 330, artifact removal module 340, or
the like, or a combination thereof.
[0095] Reconstruction module 330 may perform image reconstruct. The
image(s) subject to the reconstruction may be a projection image
(e.g., a mammary gland projection image, etc.) or a processed
projection image (e.g., a processed mammary gland projection image,
etc.), or the like, or a combination thereof. The projection image
may be generated by imaging device 110 or retrieved from another
source (e.g., database 150, and a storage, etc.). The processed
projection image may be generated by pre-procession module 320 or
retrieved from another source (e.g., database 150, and a storage,
etc.). In some embodiments, the projection image may be generated
by imaging device 110, and the processed image may be generated by
pre-procession module 320. In some embodiments, the projection
image may be a mammary gland projection image obtained by image
device 110 and a processed image may be a processed mammary gland
image generated by pre-procession module 320.
[0096] As illustrated in FIG. 3C, the reconstruction module 330 may
include a filtered a projection image generation unit 331, an
artifact correction unit 333, and a back projection unit 335.
Filtered a projection image generation unit 331 may generate a
filtered projection image including a highlighted artifact and an
X-ray attenuation artifact. Artifact correction unit 333 may
correct the highlighted artifact and the X-ray attenuation artifact
in the filtered projection image to generate a first image. Back
projection unit 335 may perform back projection to generate the
reconstructed image based on the first image.
[0097] Reconstruction module 330 may generate a reconstructed image
based on acquired images. The reconstructed image may include one
or more tomographic images. The reconstructed image may be a 3D
image. In some embodiments, the reconstructed image may be a 3D
tomographic mammary gland reconstructed image. The reconstructed
image generated by reconstruction module 330 may be sent to other
component(s) in image processing device 120, for example,
pre-procession module 320, artifact removal module 340, or the
like, or a combination thereof. The reconstructed image may be sent
to one or more components in reconstruction system 100, for
example, terminal 130, display 140, database 150, or the like, or a
combination thereof.
[0098] Reconstruction module 330 may perform image reconstruction
based on an image reconstruction method. The image reconstruction
method may include filtered back projection (FBP), a simultaneous
iterative reconstruction technique (SIRT), matrix inversion
tomosynthesis (MITS), iterative maximum a posteriori statistical
reconstruction, a Bayesian-based interactive reconstruction method,
or the like, or a combination thereof. In some embodiments,
reconstruction module 330 may reconstruct a mammary gland
reconstructed image utilizing a filtered back projection method.
More descriptions regarding filtered back projection may be found
elsewhere in the present disclosure. See, for example, FIG. 4 and
FIG. 8, and the description thereof.
[0099] As illustrated in FIG. 3D, artifact removal module 340 may
include a detector edge artifact removal unit 341, a mammary gland
edge artifact removal unit 343, and a serrated artifact removal
unit 345. Detector edge artifact removal unit 341, mammary gland
edge artifact removal unit 343, and serrated artifact removal unit
345 may be connected to or communicated with each other. Artifact
removal module 340 may be connected to or communicated with other
component(s) in image processing device 120, for example,
initialization module 310, pre-precession module 320, or
reconstruction module 330, or the like, or a combination thereof.
Artifact removal module 340 may be connected to or communicated
with unit in reconstruction system 100, for example, terminal 130,
display 140, database 150, or the like, or a combination
thereof.
[0100] Artifact removal module 340 may remove artifact in a
reconstructed image. The reconstructed image may be generated by
reconstruction module 330 or retrieved from another source (e.g.,
database 150, and a storage, etc.). The reconstructed image may
include one or more tomographic images that may depict one or more
layers of a projection object. In some embodiments, the
reconstructed image may be a mammary gland reconstructed image.
[0101] Artifact may be any error in a perception or representation
in a reconstructed image. Artifact may include detector edge
artifact, mammary gland edge artifact, artifact caused by the
movement of a patient, metal artifact, artifact caused by the
arcing of a radiation source (e.g., a bulb, etc.), artifact caused
by a deviation of a detector from its normal operation condition,
or the like, or a combination thereof. An artifact may have a
regular shape (for example, streaking, ring, serration, etc.), or
an irregular, or the like, or a combination thereof. In some
embodiments, artifacts may include detector edge artifact, mammary
gland edge artifact, serrated artifact, or the like, or a
combination thereof.
[0102] Artifact removal module 340 may remove artifact utilizing
various artifact removing methods. The artifact removing method may
include a polynomial interpolation method, an iterative deblurring
method, an expectation-maximization method, an algebraic
reconstruction technique, a Markov random field method, a wavelet
method, an ordered subsets convex iterative method, a beam-stop
technique, a scanning lead-strip technique, or the like, or a
combination thereof.
[0103] Detector edge artifact removal unit 341 may remove detector
edge artifact. Detector edge artifact may have a strip shape, as
shown in area 1210 in FIG. 12. Detector edge artifact may be caused
by a dark current, a gain, a nonlinear error, a radiation damage,
response nonuniformity, detector afterglow, or the like, or a
combination thereof. Detector edge artifact may be removed by
setting a gray value in an area of detector edge artifact based on
the gray value of pixels in a neighborhood of the detector edge
artifact. More descriptions regarding removing detector edge
artifact may be found elsewhere in the present disclosure. See, for
example, FIG. 9 and the description thereof.
[0104] A mammary gland tomographic image whose detector edge
artifact have been removed by detector edge artifact removal unit
341 may still include a serrated artifact, as shown in area 1410 in
FIG. 14. The serrated artifact may be removed by serrated artifact
removal unit 345. In some embodiments, serrated artifact may be
removed based on an intersection point corresponding to a detector
edge and a mammary gland edge, and corresponding dislocation
information. More descriptions regarding removing serrated artifact
may be found elsewhere in the present disclosure. See, for example,
FIG. 10 and FIG. 11, and the description thereof.
[0105] Mammary gland edge artifact removal unit 343 may remove
mammary gland edge artifact. FIG. 21 illustrates a mammary gland
reconstructed image. As shown in FIG. 21, there are mammary gland
edge artifact in area 2110 and area 2130. More descriptions
regarding removing mammary gland edge artifact may be found
elsewhere in the present disclosure. See, for example, FIG. 9 and
the description thereof.
[0106] It should be noted that the descriptions above in relation
to image processing device 120 is provided for the purposes of
illustration, and not intended to limit the scope of the present
disclosure. For persons having ordinary skills in the art, various
variations and modifications may be conducted under the guidance of
the present disclosure. However, those variations and modifications
do not depart the scope of the present disclosure. For example,
reconstruction module 330 may include a filtered back projection
unit (no shown in figures) that may perform filtered back
projection of a mammary gland projection image. As another example,
artifact removal module 340 may include one or more additional
artifact removal units (no shown in figures) that may remove one or
more other kinds of artifact (e.g., artifact caused by the movement
of a patient, metal worn by a patient when the patient is scanned,
the arcing of a bulb, and the deviation of a detector from its
normal operation condition, etc.). In some embodiments, the
projection object may be an organism, and an organ (e.g., a mammary
gland, a hand, a head, a lung, etc.), or the like, or a combination
thereof.
[0107] FIG. 4 illustrates a flowchart illustrating an exemplary
process 400 for image reconstruction in accordance with some
embodiments of the present disclosure. In some embodiments, process
400 may be performed by one or more devices (e.g., image processing
device 120) in image reconstruction system 100 (shown in FIG. 1)
and image processing device 120 (shown in FIG. 3A). In some
embodiments, at least part of process 400 may be performed by
computer 200 shown in FIG. 2B.
[0108] In 410, one or more parameters may be initialized or
adjusted. The parameter initialization or adjustment in 410 may be
performed by initialization module 310 of FIG. 3A. The parameters
may be related to the configuration of image reconstruction system
100. For example, the parameters may be related to imaging device
110, image processing device 120, and terminal 130, or the like, or
a combination thereof.
[0109] The parameters may be obtained from imaging device 110,
image processing device 120, terminal 130, database 150, or the
like, or a combination thereof. The parameters may be determined
based on data obtained from, for example, imaging device 110, image
processing device 120, terminal 130, database 150, or the like, or
a combination thereof. Detailed descriptions about the parameters
may be found elsewhere in the present disclosure. See, for example,
FIG. 3A and the description thereof. In some embodiments, the
parameters may be a physical coordinate of image reconstruction
system 100 and an image parameter of a projection image.
[0110] In 420, one or more projection images may be obtained. The
projection image(s) may be obtained by imaging device 110 or
retrieved from another source (e.g., database 150, a storage,
etc.). In some embodiments, the projection image may be a mammary
gland projection image.
[0111] In 430, the projection image may be a processed projection
image that has been subject to one or more pre-processing
operations. Pre-processing in 430 may be performed by
pre-procession module 320 illustrated in FIG. 3A. A projection
image may be pre-processed utilizing a pre-processing method
including, for example, image segmentation, image negative film
generation, geometrical error removal, color correction, geometric
transformation, image noise reduction, image enhancement, image
compression, the like, or a combination thereof. In some
embodiments, a projection image may be a mammary gland projection
image. More descriptions regarding methods of pre-processing a
projection image may be found elsewhere in the present disclosure.
See, for example, FIG. 5 and the description thereof.
[0112] In 440, the processed projection image may be reconstructed
to generate a reconstructed image. Image reconstruction in 440 may
be performed by reconstruction module 330 of FIG. 3A. The
reconstructed image may include one or more tomographic images that
may depict one or more layers of a projection object. The processed
projection image may be reconstructed utilizing a reconstruction
method. Exemplary reconstruction method may include filtered back
projection, a simultaneous iterative reconstruction technique
(SIRT), matrix inversion tomosynthesis (MITS), iterative maximum a
posteriori statistical reconstruction, a Bayesian-based interactive
reconstruction method, or the like, or a combination thereof.
[0113] Merely by way of example, a processed projection image may
be a processed mammary gland projection image, and a reconstructed
image may be a mammary gland reconstructed image. The processed
mammary gland projection image may be reconstructed utilizing
filtered back projection. More descriptions regarding back
projection reconstruction method may be found elsewhere in the
present disclosure. See, for example, FIG. 8 and the description
thereof.
[0114] In 450, artifact in the reconstructed image may be removed.
Artifact removal in 450 may be performed by artifact removal module
340 of FIG. 3A. The artifact may be take the form of any shape
and/or type. More descriptions regarding artifact may be found
elsewhere in the present disclosure. See, for example, FIG. 3A and
the description thereof.
[0115] Artifact in a reconstructed image may be removed utilizing
an artifact removing method. The artifact removing method may
include a polynomial interpolation method, an iterative deblurring
method, an expectation-maximization method, an algebraic
reconstruction technique, a Markov random field method, a wavelet
method, an ordered subsets convex iterative method, a beam-stop
technique, a scanning lead-strip technique, or the like, or a
combination thereof.
[0116] In some embodiments, a reconstructed image may be a mammary
gland reconstructed image. Artifact in a reconstructed image may
include detector edge artifact, mammary gland edge artifact,
serrated artifact, or the like, or a combination thereof. More
descriptions regarding artifact removing method may be found
elsewhere in the present disclosure. See, for example, FIG. 9 and
the description thereof.
[0117] It should be noted that process 400 described above is
provided for the purposes of illustration, and not intended to
limit the scope of the present disclosure. Apparently for persons
having ordinary skills in the art, numerous variations and
modifications may be conducted under the teaching of the present
disclosure. However, those variations and modifications do not
depart the protection scope of the present disclosure. In some
embodiments, some steps may be reduced or added. For example, 430
may be omitted. A reconstructed image may be generated based on an
original projection image without pre-processing. As another
example, 450 may be omitted. In some embodiments, the projection
object may be an organism, an organ (e.g., a mammary gland, a hand,
a head, a lung, etc.), or the like, or a combination thereof.
Similar modifications should fall within the scope of the present
disclosure.
[0118] FIG. 5 is a flowchart illustrating an exemplary process 500
for pre-processing projection image in accordance with some
embodiments of the present disclosure. In some embodiments, process
500 may be performed by pre-procession module 320 in imaging
processing device 120 (shown in FIG. 3A and FIG. 3B). In some
embodiments, process 500 described with reference to FIG. 5 may be
an exemplary process for achieving 430 shown in FIG. 4.
[0119] In 510, a projection image may be segmented to obtain a
segmented projection image including a region of interest. Image
segmentation in 510 may be performed by segmentation unit 321 of
FIG. 3B. The projection image may be obtained by imaging device 110
or retrieved from another source (e.g., a database 150, a storage,
etc.). The projection image may be segmented utilizing an image
segmentation method. The image segmentation method may include an
edge detecting method, a threshold segmenting method, a
histogram-based method, a clustering method, a compression-based
method, a region-growing method, a graph partitioning method, or
the like, or a combination thereof.
[0120] An edge detection method may be performed based on an edge
detection algorithm. The edge detection algorithm may include, for
example, the Sobel edge detection algorithm, the Canny edge
detection algorithm, a phase congruency-based algorithm, or the
like, or a combination thereof.
[0121] A threshold segmenting method may be performed by
classifying pixels in an image based on a fixed pixel value. For
example, a pixel may be deemed as a black pixel if its pixel value
exceeds the fixed pixel value; a pixel may be deemed a white pixel
if its pixel value is smaller than the fixed pixel value.
[0122] A region-growing method may also be referred as a seed
filling method. A region-growing method may be performed by
selecting one or more seeds and determining whether one or more
neighboring pixels of the selected seeds may be added to the
region.
[0123] A histogram-based method may be performed by determining a
gray value histogram based on the gray value of pixels in an image.
One or more peaks and valleys in a histogram may be used to
determine an edge of a region of interest in the image.
[0124] In some embodiments, a projection image may be a mammary
gland projection image. The mammary gland projection image may be
segmented to obtain a region of mammary gland. The mammary gland
projection image may be segmented utilizing the region-growing
method that may be also be referred to as the seed filling method.
More descriptions regarding region-growing method may be found
elsewhere in the present disclosure. See, for example, FIG. 7 and
the description thereof.
[0125] In 520, a segmented projection image may be processed to
generate a negative film. Negative film operation in 520 may be
performed by negative film unit 323 of FIG. 3B. A negative film may
be an image in which the darkness of a portion of the projection
object reversely relates to the darkness of the same portion in the
film or image. For instance, in a negative film, a lightest area of
the projection object appears darkest in the film, and a darkest
area of the projection object appears lightest in the film.
[0126] In some embodiments, 520 may include one or more of the
following operations. A maximum gray value Max_A in a segmented
projection image may be determined. A corrected gray value of each
pixel in the segmented projection image may be determined by
subtracting its gray value from Max_A. The corrected gray value of
a pixel may be assigned to the pixel as its gray value.
[0127] In 530, a geometrical error of the negative film of a
segmented projection image may be corrected. Geometrical error
correction operation in 530 may be performed by geometrical error
correction unit 325 of FIG. 3B. A geometrical error may include,
for example, a translation error of the detector, a rotation error
of the detector, or the like, or a combination thereof.
[0128] A translation error of the detector may be caused by the
translation of the detector in a horizontal direction. As used
herein, "horizontal direction" may refer to a direction along the
x-y plane shown in FIG. 2A. In some embodiments, the translation
error of the detector may be removed by one or more of the
following operations. A coordinate matrix of the pixels in a
segmented projection image in a first coordinate system may be
obtained. A translation vector of a pixel in the first coordinate
system and in a second coordinate system may be determined. In some
embodiments, the translation vector may be determined by
subtracting the coordinate of a pixel in the first coordinate
system from its coordinate in the second coordinate system. A
corrected coordinate matrix of pixels of the segmented projection
image in the second coordinate system may be determined based on
the coordinate matrix of the first coordinate system and the
translation vector.
[0129] A rotation error of the detector may be caused by a rotation
of the detector about a vertical direction. As used herein,
"vertical direction" may refer to a direction along the z-axis
shown in FIG. 2A. In some embodiments, a rotation error of the
detector may be removed by one or more of the following operations.
A mapping relationship between a coordinate matrix of pixels in a
segmented projection image in a first coordinate system and its
coordinate system in a second coordinate system may be determined.
A coordinate of each pixel in the second coordinate system may be
determined based on the mapping relationship. A gray value of each
pixel in the segmented projection image may be determined by
utilizing an interpolation algorithm. The interpolation algorithm
may include an image interpolation algorithm, a bilinear
interpolation algorithm, a recent field interpolation algorithm, or
the like, or a combination thereof.
[0130] It should be noted that process 500 described above is
provided for the purposes of illustration, and not intended to
limit the scope of the present disclosure. Apparently for persons
having ordinary skills in the art, numerous variations and
modifications may be conducted under the teaching of the present
disclosure. However, those variations and modifications do not
depart the protecting scope of the present disclosure. In some
embodiments, some steps may be reduced or added. For example, 520
may be omitted. As another example, 530 may be omitted. In some
embodiments, 510, 520, and 530 may be performed in any order. For
example, 520 may be performed before 510. A projection image may be
processed to generate a negative film first and then be segmented.
As a further example, 530 may be performed before 510 and 520.
Similar modifications should fall within the scope of the present
disclosure.
[0131] FIG. 6 is a flowchart illustrating an exemplary process 600
for segmenting a projection image in accordance with some
embodiments of the present disclosure. In some embodiments, process
600 may be performed by pre-procession module 320 in imaging
processing device 120 (shown in FIG. 3A). In some embodiments,
process 600 described with reference to FIG. 6 may be an exemplary
process for achieving 510 shown in FIG. 5.
[0132] In 610, an average gray value of one or more pixels of a
projection image may be determined. The projection image may be
obtained by imaging device 110 or retrieved from another source
(e.g., a database 150, a storage, etc.). In some embodiments, the
projection image may be a mammary gland projection image.
[0133] In 630, one of two marks, e.g., mark A or mark B, may be
assigned to one or more pixels of the projection image based on the
relationship between the gray value of a pixel and the average gray
value. Merely by way of example, mark A may correspond to the value
of 0, and mark B may correspond to the value of 1.
[0134] The relationship between the gray value of a pixel and the
average gray value may be determined according to any rule. In some
embodiments, the relationship may be determined according numerical
values of the gray value of a pixel and the average gray value. For
example, A may be assigned to a pixel when its gray value is less
than the average gray value. B may be assigned to a pixel when its
gray value is not less than average gray value. As another example,
A may be assigned to a pixel when the difference between its gray
value and the average gray value is not less than a first number,
for example, 1, or 5, or 10, or the like. B may be assigned to a
pixel when the difference between its gray value and the average
gray value is less than a second number, for example, 1, or 4, or
7, and the like. The first number may be the same as or different
from the second number.
[0135] In 650, the boundary of a region of interest may be
determined based on the marks of pixels in the projection image. A
region of interest may also be referred to as a target area. In
some embodiments, a region of interest may be a region of a mammary
gland in a projection image. The boundary of the region of interest
may be determined utilizing an edge detecting method, a threshold
segmenting method, a histogram-based method, a clustering method, a
compression-based method, a region-growing method, a graph
partitioning method, or the like, or a combination thereof. More
descriptions regarding region-growing method may be found elsewhere
in the present disclosure. See, for example, FIG. 7 and the
description thereof.
[0136] In some embodiments, a segmented projection image may
include a region of interest determined by process 600. In some
embodiments, a segmented projection image may include a segmented
region based on a plurality of regions of interest. The plurality
of regions of interest may be determined by process 600,
respectively, based on a plurality of projection images.
[0137] A segmented region may be determined by various ways. In
some embodiments, the segmented region may be a union of a
plurality of regions of interest. As shown in FIG. 19, a1, a2, and
a3 illustrate three regions of interest, and b illustrates the
overlapping of a1, a2, and a3. A segmented region is region c,
which is a union of a1, a2, and a3. In some embodiments, the
segmented region may be determined based on the coordinates of
pixels in regions of interest. As shown in FIG. 20, the segmented
region may be rectangle S whose diagonal vertexes may be M(X.sub.1,
Y.sub.1) and N(X.sub.2, Y.sub.2). X.sub.1 may be the largest
horizontal coordinate value of all pixels of the plurality of
regions of interest. Y.sub.1 may be the smallest longitudinal
coordinate value of all pixels of the plurality of regions of
interest. X.sub.2 may be the smallest horizontal coordinate value
of all pixels of the plurality of regions of interest. Y.sub.2 may
be the largest longitudinal coordinate value of all pixels of the
plurality of regions of interest. In some embodiments, X.sub.2 may
be 0 if the projection image is taken when a patient is standing.
In some embodiments, Y.sub.1 may be 0 if the projection image is
taken when the patient is lying. In some embodiments, X.sub.1 may
be the largest horizontal coordinate value of all pixels of the
plurality of regions of interest. Y.sub.1 and X.sub.2 may be 0.
Y.sub.2 may be the largest height of the plurality of projection
images.
[0138] It should be noted that process 600 described above is
provided for the purposes of illustration, and not intended to
limit the scope of the present disclosure. Apparently for persons
having ordinary skills in the art, numerous variations and
modifications may be conducted under the teaching of the present
disclosure. However, those variations and modifications do not
depart the protecting scope of the present disclosure. In some
embodiments, in 610, the median value or mode or any other
statistic of the gray values of one or more pixels of a projection
image may be determined based on the gray value of the one or more
pixels of the projection image. In 630, a mark may be assigned to a
pixel of a projection image based on the relationship between the
gray value of the pixel and the median value or mode or any other
statistic parameter of the gray values of one or more pixels of a
projection image. In some embodiments, in 630, any number of marks
may be assigned to a pixel of a projection image based on the
relationship between the gray value of a pixel and the average gray
value. For example, the number of marks that may be assigned to a
pixel may be three, four, five, or the like, or a combination
thereof. Similar modifications should fall within the scope of the
present disclosure.
[0139] FIG. 7 is a flowchart illustrating an exemplary process 700
for determining the boundary of a region of interest in accordance
with some embodiments of the present disclosure. Process 700 may
also be referred to as a region-growing method or a seed filling
method. In some embodiments, process 700 may be performed by
pre-procession module 320 in imaging processing device 120. In some
embodiments, process 700 described with reference to FIG. 7 is an
exemplary process for achieving 650 shown in FIG. 6.
[0140] In 710, a seed pixel, or referred to as a seed, may be
determined from one or more pixels of a projection image. The seed
pixel may be any pixel in the projection image. In some
embodiments, the seed pixel may be a pixel whose gray value is not
less than the average gray value, which may be assigned mark B
according to the example described above. In some embodiments, the
seed pixel may be a pixel in the lower left corner or the upper
left corner of a projection image and whose mark is B.
[0141] In 720, pixels in a M.times.N.times.Q neighborhood of the
seed pixel may be searched. M, N, and Q may be a positive integer
of any value. At least two of M, N, and Q may be equal to each
other, or different from each other. In some embodiments, M, N, and
P may equal to 3. Some or all pixels in the M.times.N.times.Q
neighborhood of the seed pixel may be searched. Merely by way of
example, 8 pixels may be searched in the M.times.N.times.Q
neighborhood of the seed pixel. As another example, 4 pixels may be
searched in the M.times.N.times.Q neighborhood of the seed
pixel.
[0142] In 730, a judgment may be made as to whether there is a
pixel marked as A in the M.times.N.times.Q neighborhood of the seed
pixel. According to the example already described, a pixel may be
assigned to mark A when its gray value is less than the average
gray value (see 630). If there is a pixel that is assigned mark A
in the M.times.N.times.Q neighborhood of the seed pixel, 740 may be
performed. Otherwise, 750 may be performed.
[0143] In 740, the pixel in the M.times.N.times.Q neighborhood of
the seed pixel and assigned mark A may be recorded as a boundary
pixel. The boundary pixel may be located on the boundary of region
of interest.
[0144] In 750, the pixel in M.times.N.times.Q neighborhood of seed
pixel may be recorded as an internal pixel. The internal pixel may
be located inside the region of interest.
[0145] In 760, a judgment may be made as to whether there is a
pixel that is assigned mark B in the M.times.N.times.Q neighborhood
of the seed pixel and has not been recorded either as an internal
pixel or a boundary pixel. As described above with reference to
630, a pixel is assigned mark B when its gray value is not less
than the average gray value. If there is a pixel in the
M.times.N.times.Q neighborhood of the seed pixel that is assigned
mark B and has not be recorded either as an internal pixel or a
boundary pixel, 780 may be performed. Otherwise, 770 may be
performed.
[0146] In 770, the search for a pixel in the M.times.N.times.Q
neighborhood of the seed pixel may be finished.
[0147] In 780, the pixel in the M.times.N.times.Q neighborhood of
the seed pixel that is marked as B and has not be recorded may be
designated as a seed pixel. The operations in 720 to 770 may be
repeated until all pixels in the M.times.N.times.Q neighborhood of
the seed pixel have been recorded and the search may terminate.
[0148] Process 700 may identify one or more boundary pixels in the
projection image. The boundary of a region of interest may be
determined by connecting adjacent boundary pixels. In some
embodiments, the boundary of the region of interest may be a
maximum boundary connecting adjacent boundary pixels. For instance,
if there are more than one way of connecting two adjacent boundary
pixels, the shortest connection may be designated as the section of
boundary connecting the two adjacent boundary pixels. As another
example, if there are more than one way of connecting two adjacent
boundary pixels, the connection whose resultant region of interest
has a largest area may be designated as the section of boundary
connecting the two adjacent boundary pixels.
[0149] In some embodiments, a projection image in process 700 may
be a mammary gland projection image. A boundary of the mammary
gland in a projection image may be determined by performing process
700.
[0150] It should be noted that process 700 described above is
provided for the purposes of illustration, and not intended to
limit the scope of the present disclosure. Apparently for persons
having ordinary skills in the art, numerous variations and
modifications may be conducted under the teaching of the present
disclosure. However, those variations and modifications do not
depart the protecting scope of the present disclosure. In some
embodiments, any number of seed pixels (e.g., 3, 5, 10, etc.) may
be determined from one or more pixels of a projection image.
[0151] FIG. 8 is a flowchart illustrating an exemplary process 800
for generating a reconstructed image in accordance with some
embodiments of the present disclosure. Process 800 may also be
referred to as filtered back projection. In some embodiments,
process 800 may be performed by reconstruction module 330 of
imaging processing device 120 shown in FIG. 3A and FIG. 3C. In some
embodiments, process 800 described with reference to FIG. 8 may be
an exemplary process for achieving 440 shown in FIG. 4.
[0152] In 810, a projection image from one or more projection
angles may be filtered to generate a filtered projection image.
Filtered projection image generation operation in 810 may be
performed by filtered projection image generation unit 331 of FIG.
3C. The projection image may be filtered according to a filter
algorithm. The filter algorithm may include the Ramp-Lak filter
algorithm, the Shepp-Logan filter algorithm, the Hamming filter
algorithm, or the like, or a combination thereof. In some
embodiments, the filtered projection image may include artifact
such as a highlighted artifact, an X-ray attenuation artifact, a
detector edge artifact, a mammary edge artifact, a serrated
artifact, or the like, or a combination thereof.
[0153] In 830, the highlighted artifact in the filtered projection
image may be corrected. Highlighted artifact correction operation
in 830 may be performed by artifact correction unit 333 of FIG. 3C.
The highlighted artifact may take the form of a highlight edge
around the projection object in a projection image. The highlighted
artifact may be caused by filtering.
[0154] In 850, an X-ray attenuation artifact in the filtered
projection image may be corrected. X-ray attenuation artifact
correction operation in 850 may be performed by artifact correction
unit 333 of FIG. 3C. The X-ray attenuation artifact may be caused
by difference in activities between X-ray photons. As described
with reference to FIG. 2, radiation emitted by radiation source 201
may pass through a projection object and reach detector 203 to
generate a projection image on detector 203. As X-ray passes
through the projection object, low energy X-ray photons may be
attenuated more, and the remaining high energy photons may be
attenuated less than low energy photons. Such a difference in
photon attenuation may cause X-ray attenuation artifact in the
projection image.
[0155] In 870, a reconstructed image may be generated by performing
a back projection operation on the filtered projection image. Back
projection operation in 870 may be performed by back projection
unit 335 of FIG. 3C. The reconstructed image may include one or
more tomographic images. Back projection may be performed based on
the inverse transformation of each view through a filtered
projection image in the direction it was originally acquired. As
used herein, "view" may refer to an angle at which a projection
image is obtained.
[0156] In some embodiments, the projection image in process 800 may
be a mammary gland projection image. The reconstructed image may be
a reconstructed mammary gland projection image. A plurality of
mammary gland projection images may be processed based on the
filtered back projection operation to generate a reconstructed
mammary gland projection image.
[0157] It should be noted that process 800 described above is
provided for the purposes of illustration, and not intended to
limit the scope of the present disclosure. Apparently for persons
having ordinary skills in the art, numerous variations and
modifications may be conducted under the teaching of the present
disclosure. However, those variations and modifications do not
depart the protecting scope of the present disclosure. In some
embodiments, some steps may be omitted or added. For example, 830
or 850 may be omitted. In some embodiments, 810, 830, and 850 may
be performed in any order. For example, 830 and 850 may be
performed at the same time. As another example, 850 may be
performed before 830. As a further example, 830 and/or 850 may be
performed before 810. In some embodiments, the projection object
may be an organism, and an organ (e.g., a mammary gland, a hand, a
head, a lung, etc.), or the like, or a combination thereof.
[0158] FIG. 9 is a flowchart illustrating an exemplary process 900
for removing artifact in a reconstructed image in accordance with
some embodiments of the present disclosure. In some embodiments,
process 900 may be performed by artifact removal module 340 of
imaging processing device 120 shown in FIG. 3A and FIG. 3D. In some
embodiments, process 900 described with reference to FIG. 9 may be
an exemplary process for achieving 450 shown in FIG. 4.
[0159] In 910, a detector edge artifact may be removed. The
artifact removal in 910 may be performed by detector edge artifact
removal unit 341. More descriptions regarding a detector edge
artifact may be found elsewhere in the present disclosure. See, for
example, FIG. 3A and the description thereof.
[0160] The detector edge artifact may be removed by setting the
gray values of the pixels in a detector edge artifact based on the
gray values of pixels in the neighborhood of the area of a detector
edge artifact. In some embodiments, the process for removing a
detector edge error may include one or more of the following
operations. The neighborhood area of a detector edge artifact may
be determined. The neighborhood area may be an area close to the
detector edge artifact and outside of the detector edge artifact.
The neighborhood area may be an area of any size or shape. The
average gray value of the pixels in the neighborhood area may be
determined. In some embodiments, the pixels in the detector edge
artifact in the tomographic image (e.g., a same slice of a CT
image, etc.) may be assigned a same gray value. For instance, the
gray values of the pixels in the detector edge artifact may be
assigned the average gray value of pixels in the neighborhood
area.
[0161] In 930, a projection object edge artifact may be removed. In
some embodiments, the projection object may include a mammary
gland. In some embodiments, the projection object edge may include
the edge of the mammary gland. In some embodiments, the process for
removing a mammary gland edge artifact may include one or more of
the following operations. The boundary of the projection object in
one or more projection images from one or more views may be
determined by an edge detection algorithm. The edge detection
algorithm may include, for example, the Sobel edge detection
algorithm, the Canny edge detection algorithm, a phase
congruency-based algorithm, the Otsu's algorithm, or the like, or a
combination thereof. For example, the boundary of a projection
object may be detected by the Otsu's algorithm first and then by
the Sobel edge detection algorithm. A 3D projection object surface
may be generated based on one or more projection images from one or
more views using a simultaneous algebraic reconstruction technique
(SART). The pixel value distribution of each projection image from
a projection view may be updated based on the boundary of a
projection image. The gray value of the pixels outside of the
region of the projection object may be set as 0 after each
iteration in SART. A pixel may be determined to be outside of the
region of the projection object based on the 3D projection object
surface.
[0162] In some embodiments, the projection object may be a mammary
gland. The artifact removal in 930 may be performed by mammary
gland edge artifact removal unit 343.
[0163] In 950, a serrated artifact may be removed. The artifact
removal in 950 may be performed by serrated artifact removal unit
345 shown in FIG. 3A. More descriptions regarding a serrated
artifact removal method may be found elsewhere in the present
disclosure. See, for example, FIG. 10 and FIG. 11, and the
description thereof.
[0164] It should be noted that process 900 described above is
provided for the purposes of illustration, and not intended to
limit the scope of the present disclosure. Apparently for persons
having ordinary skills in the art, numerous variations and
modifications may be conducted under the teaching of the present
disclosure. However, those variations and modifications do not
depart the protecting scope of the present disclosure. In some
embodiments, some steps may be reduced or added. For example, 930
may be omitted. In some embodiments, 930 and 910 may be performed
at the same time. In some embodiments, 930 may be performed before
910. In some embodiments, one or more steps may be added to remove
one or more other artifacts including, for example, an artifact
caused by the movement of the patient, metal worn by the patient
when the patient is scanned, the arcing of the radiation source
(e.g., a bulb, etc.), a deviation of the detector from its normal
operation condition, or the like, or a combination thereof. In some
embodiments, the projection object may be an organism, and an organ
(e.g., a mammary gland, a hand, a head, a lung, etc.), or the like,
or a combination thereof.
[0165] FIG. 10 is a flowchart illustrating an exemplary process
1000 for removing serrated artifact in accordance with some
embodiments of the present disclosure. In some embodiments, process
1000 may be performed by serrated artifact removal unit 345 of
imaging processing device 120 shown in FIG. 3A. In some
embodiments, process 1000 described with reference to FIG. 10 may
be an exemplary process for achieving 950 as shown in FIG. 9.
[0166] In some embodiments, a serrated artifact may be present in a
reconstructed image. FIG. 13 illustrates a mammary gland
reconstructed image. As shown in FIG. 13, serrated artifacts are
present in region 1310. FIG. 14 illustrates a mammary gland
reconstructed image without serrated artifact correction. As shown
in FIG. 14, serrated artifacts are present in area 1410 and 1420.
The mammary gland reconstructed image shown in FIG. 15A and FIG.
15B includes serrated artifact S (e.g., S1, S2, S3, S4, S1', S2',
S3', S4').
[0167] In 1010, the mapping position of the detector edge in a
projection image from a projection view with respect to the
detector edge in a corresponding tomographic image may be
determined. A tomographic image may be part of a reconstructed
image. The reconstructed image may include one or more tomographic
images. A tomographic image may depict a layer of the projection
object. In some embodiment, a tomographic image may be a mammary
gland tomographic image that may depict a layer of the mammary
gland. FIG. 15A depicts an upper portion of a mammary gland and
FIG. 15B depicts a lower portion of the mammary gland. Horizontal
dotted lines L1-L4 and L1'-L4' depict mapping positions of the
detector edge.
[0168] In some embodiments, 1010 may include one or more of the
following operations. A first geometric position relationship
between radiation source 201 and detector 203 (shown in FIG. 2A)
may be determined. A second geometric position relationship between
a projection image from a projection view and a corresponding
tomographic image may be determined. The mapping coordinates of a
pixel in the projection image with respect to the corresponding
pixel in the corresponding tomographic image may be determined
based on the first geometric position relationship and the second
geometric position relationship. As used herein, a pixel in a
projection image and the corresponding pixel in a corresponding
tomographic image may relate to a same portion (e.g., a same spot,
etc.) of the projection object. The mapping coordinates of a pixel
in the projection image with respect to the corresponding pixel in
the tomographic image may be determined utilizing, for example, an
image interpolation algorithm, a bilinear interpolation algorithm,
a recent field interpolation algorithm, or the like, or a
combination thereof. According to a bilinear interpolation
algorithm, the mapping coordinates of a pixel may be determined
based on the coordinates of two neighboring pixels. In a recent
field interpolation algorithm, the mapping coordinates of a pixel
may be determined based on the coordinates of a neighboring pixel
closest to the pixel. The mapping position of the detector edge in
the projection image with respect to the detector edge in the
corresponding tomographic image may be determined based on an
imaging area of the detector in the projection image and the
mapping coordinates of the pixels of the detector edge in the
projection image.
[0169] For example, the resolution of a projection image may be
1000*1000. The mapping coordinates of a pixel in the projection
image with respect to the corresponding pixel in the corresponding
tomographic image may be smaller than 0 or larger than 1000. A
pixel whose mapping coordinates are smaller than 0 or larger than
1000 may be a pixel outside of the imaging area of the detector.
The detector edge may be determined by a critical value (e.g., 0
and 1000). As shown in FIG. 15A and FIG. 15B, line L (e.g., L1, L2,
L3, L4, L1', L2', L3' and L4') includes the mapping position
corresponding to the detector edge. In some embodiments, a point
(x, y) on the line L may be described in a two-dimensional array.
For example, (1, 2) may describe a point with a horizontal
coordinate of 1 and a vertical coordinate of 2 on the line L.
[0170] In 1020, a projection object edge in the tomographic image
may be determined. The projection object edge may be a boundary
between an area of projection object and a direct exposing area. As
used herein, a direct exposing area may refer to an area of air
(e.g., an area outside of a projection object, etc.).
[0171] In some embodiments, a projection object may be a mammary
gland. The mammary gland edge in a tomographic image may be the
boundary between region of the mammary gland and a region outside
of the mammary gland (i.e., a direct exposing area). For example,
as shown in FIG. 15A, the gray area may depict a region
corresponding to a mammary gland (i.e., a region of mammary gland),
and the dark area may depict the region outside of the mammary
gland (i.e., a direct exposing area). There is a boundary with a
serrated line between the gray area and the dark area. The mammary
gland edge may be the boundary between the gray area and the dark
area.
[0172] In 1030, an intersection point corresponding to the
projection object edge and the mapping position corresponding to
the detector edge may be determined.
[0173] As shown in FIG. 15A, Point P (e.g., P1, P2, P3, and P4) is
an intersection point corresponding to the mammary gland edge (the
boundary between the gray area and the dark area) and the mapping
position corresponding to the detector edge (the horizontal dotted
lines L1, L2, L3, and L4). Artifact S (e.g., S1, S2, S3, and S4)
has a shape of serrations, and referred to as a serrated artifact.
Point P1, P2, P3, and P4 may be roughly horizontal to serrated
artifact S1, S2, S3, and S4.
[0174] In 1040, dislocation information of each intersection point
may be determined based on the intersection point and the mapping
position of the detector edge. The dislocation information may be a
distance between the intersection point and an edge point of a
corresponding serrated artifact. The corresponding serrated
artifact may be the serrated artifact that is roughly horizontal to
the intersection point.
[0175] For example, in FIG. 15A, the corresponding serrated
artifact of intersection point P1 may be serrated artifact S1.
Dislocation information of the intersection point P1 may be the
distance between the intersection point P1 and the edge point of
the serrated artifact S1, which is denoted as D1.
[0176] In 1050, the serrated artifact may be removed based on the
intersection point and the dislocation information. The serrated
artifact may be removed by moving its edge for a distance towards
the region of the projection object (e.g., a mammary gland,
etc.).
[0177] In some embodiments, the distance may be equal to the
dislocation information of the intersection point. In some
embodiments, the distance may be a statistic value determined based
on the dislocation information of a plurality of intersection
points. For example, the distance may be the average value of the
dislocation information of one or more intersection points in the
reconstructed image. As another example, the distance may be the
median value of the dislocation information of one or more
intersection points in the reconstructed image.
[0178] In some embodiments, as shown in FIG. 15A, serrated artifact
S1 may be removed by moving its edge toward the region of the
mammary gland (the gray area) for a distance equal to the
dislocation information of intersection point P1. More descriptions
regarding the method to remove serrated artifact based on
intersection point and the corresponding dislocation information
may be found elsewhere in the present disclosure. See, for example,
FIG. 11 and the description thereof.
[0179] It should be noted that process 1000 described above is
provided for the purposes of illustration, and not intended to
limit the scope of the present disclosure. Apparently for persons
having ordinary skills in the art, numerous variations and
modifications may be conducted under the teaching of the present
disclosure. However, those variations and modifications do not
depart the protecting scope of the present disclosure. In some
embodiments, 1010 and 1020 may be performed at the same time. In
some embodiments, 1020 may be performed before 1010. In some
embodiments, the projection object may be an organism, and an organ
(e.g., a mammary gland, a hand, a head, a lung, etc.), or the like,
or a combination thereof.
[0180] FIG. 11 is a flowchart illustrating an exemplary process
1100 for removing a serrated artifact based on an intersection
point and relevant dislocation information in accordance with some
embodiments of the present disclosure. In some embodiments, process
1100 may be performed by serrated artifact removal unit 345 in
imaging processing device 120 as shown in FIG. 3A. In some
embodiments, process 1100 described with reference to FIG. 11 may
be an exemplary process for achieving 1050 as shown in FIG. 10.
[0181] In 1110, a projection object template of a tomographic image
may be generated. The projection object template may reduce image
processing cost. The projection object template may be generated by
setting gray values of pixels in a number of different regions of
the tomographic image. In some embodiments, the tomographic image
may have two different regions (e.g., a projection object region
and a direct exposing region) and a binary projection object
template may be used. For example, the gray value of pixels in the
projection object region may be set to E, and the gray value of
pixels out of the direct exposing region may be set to F. For
example, E may be 1 and F may be 0. In some embodiments, the
tomographic image may have more than two different regions and a
multi-value projection object template may be used. For example,
the tomographic image may have three different regions (e.g., a
soft tissue region, a bone region, and a direct exposing region)
and a three-value projection object template may be used. The
number of different regions may be any integer (e.g., one, two,
three, four, etc.).
[0182] In some embodiments, a projection object may be a mammary
gland and a mammary gland template may be used. As shown in FIG.
16A, the gray values of the pixels in a region of the mammary gland
may be set as 1, and the gray values of the pixels in a direct
exposing region may be set as 0. In that way, the region of the
mammary gland may be depicted as white, and the direct exposing
region may be depicted as black.
[0183] In 1130, a serrated artifact in the projection object
template may be removed to obtain a corrected projection object
template without the serrated artifact. The serrated artifact may
be removed in any order. In some embodiments, the serrated artifact
in the portion of the template corresponding to the region of a
projection object close to the center of the projection object may
be removed first and the serrated artifact in the portion of the
template corresponding to the region of the projection object close
to an edge of the projection object may be removed afterwards. In
some embodiments, the serrated artifact in the portion of the
template corresponding to the region of a projection object close
to the edge of the projection object may be removed first and the
serrated artifact in the portion of the template corresponding to
the region of a projection object close to the center of the
projection object may be removed afterwards.
[0184] In some embodiments, a projection object may be a mammary
gland and a mammary gland template may be used. As shown in FIG.
16A and FIG. 16B, S (e.g., S1, S2, S3, S4, S1', S2', S3' and S4')
may be a serrated artifact and E (e.g., E1, E2, E3 and E4) may be
an edge of the serrated artifact. L (e.g., L1, L2, L3 and L4) may
be the mapping position corresponding to the detector edge. P
(e.g., P1, P2, P3 and P4) may be an intersection point
corresponding to a mammary gland edge (e.g., a boundary between the
gray area and the dark area). D (e.g., D1, D2, D3 and D4) may be
dislocation information of the corresponding intersection
point.
[0185] Serrated artifact S1 to S4 in FIG. 16A may be removed in any
order (e.g., in a successive order from S1 to S4, in a reversed
order from S4 to S1, etc.). Merely by way of example, serrated
artifact S1 may be removed first and then S2, S3, and S4 may be
removed successively. Serrated artifact S1 may be removed by moving
its edge E1 towards the portion of the image corresponding to the
center of mammary gland that is on the left side of E1 in FIG. 16A
for distance D1. The moved edge of serrated artifact is E1' shown
in FIG. 16B. The gray value of the pixels in the area between E1
and E1' may be set as 0 so that the area between E1 and E1' may be
black (See FIG. 16C). In that way, serrated artifact S1 may be
removed to obtain a corrected mammary gland edge without serrated
artifact S1.
[0186] A boundary between the light area and the dark area in FIG.
16C may depict a corrected mammary gland edge after removing
serrated artifact S1. The determination of an intersection point
between the corrected mammary gland edge and the mapping position
corresponding to the detector edge may be repeated. As shown in
FIG. 16D, P2' is an intersection point corresponding to a corrected
mammary gland edge and a mapping position corresponding to detector
edge L2. Serrated artifact S2 to S4 may be successively removed
using the same way of removing serrated artifact S1.
[0187] In 1150, a corrected tomographic image may be generated
based on the tomographic image and the corrected projection object
template in which the serrated artifact is removed (or referred to
as without the serrated artifact). According to the corrected
projection object template, a corrected region outside of the
projection object (a dark area in the corrected projection object
template) may be obtained. The gray value of the pixels in the
corresponding region outside of the projection objection in a
tomographic image may be set as 0. In that way, the serrated
artifact in the tomographic image may be removed to generate a
corrected tomographic image. A projection object edge may be smooth
or essentially smooth in the corrected tomographic image.
[0188] In some embodiments, the projection object may be a mammary
gland. As shown in FIG. 17, there are serrated artifacts in mammary
gland edge in area 1710 before serrated artifacts are removed, and
there is no visible serrated artifact along the mammary gland edge
in area 1730 after serrated artifacts are removed. In some
embodiments, the projection object may be an organism, and an organ
(e.g., a mammary gland, a hand, a head, a lung, etc.), or the like,
or a combination thereof.
EXAMPLES
[0189] The following examples are provided for illustration
purposes, and not intended to limit the scope of the present
disclosure.
Example 1
[0190] FIG. 12 illustrates a reconstructed image of a mammary
gland. As shown in FIG. 12, there are detector edge artifacts in
region 1210. The detector edge artifacts are strip-shaped. The
existence of the detector edge artifacts may influence the results
of a diagnosis. In some embodiments, the detector edge artifacts
may be removed according to process 900 described with reference to
FIG. 9.
Example 2
[0191] FIG. 13 illustrates an exemplary reconstructed image of a
mammary gland. As shown in FIG. 13, there are serrated artifacts in
region 1310. The existence of the serrated artifacts may influence
the result of a diagnosis. In some embodiments, the serrated
artifacts may be removed according to process 1000 and process 1100
described with reference to FIG. 10 and FIG. 11.
Example 3
[0192] FIG. 14 illustrates an exemplary reconstructed image of
mammary gland without serrated artifact correction. As shown in
FIG. 14, the top portion of FIG. 14 depicts an upper portion of a
mammary gland, and the bottom portion of FIG. 14 depicts a lower
portion of the mammary gland. There are serrated artifacts in
region 1410 (at the upper edge of the mammary gland reconstructed
image) and region 1420 (at the bottom edge of the mammary gland
reconstructed image).
Example 4
[0193] FIG. 15A and FIG. 15B illustrate reconstructed images of a
mammary gland with serrated artifacts. FIG. 15A depicts an upper
portion of a mammary gland. FIG. 15B depicts a lower portion of the
mammary gland. Serrated artifact (e.g., S1, S2, S3, S4, S1', S2',
S3' and S4') is serration-shaped. Line L (e.g., L1, L2, L3, L4,
L1', L2', L3' and L4') is a mapping position corresponding to the
detector edge. Point P (e.g., P1, P2, P3, P4, P1', P2', P3' and
P4') is an intersection point corresponding to a mammary gland edge
(a boundary between the gray area and the dark area) and line L. D
(e.g., D1, D2, D3, D4, D1', D2', D3' and D4') is dislocation
information of intersection point P, which is the distance between
intersection point P and the edge point of a corresponding serrated
artifact.
Example 5
[0194] FIG. 16A to FIG. 16D illustrate exemplary mammary gland
templates. FIG. 16A and FIG. 16B illustrate mammary gland templates
before serrated artifact were removed. FIG. 16C and FIG. 16D
illustrate mammary gland templates after serrated artifact S1 were
removed. As shown in FIGS. 16A-16D, line L (e.g., L1, L2, L3, and
L4) is a mapping position corresponding to the detector edge. P
(e.g., P1, P2, P3, P4, and P2') is an intersection point
corresponding to a mammary gland edge (a boundary between the gray
area and the dark area) and line L. D (e.g., D1) is dislocation
information of intersection point P, which is a distance between
intersection point P and edge point of corresponding serrated
artifact. E (e.g., E1, E2, E3, and E4) is an edge of the serrated
artifact. E' (e.g., E1) is a corrected artifact edge which was
obtained by moving edge E left for the distance equal to
dislocation information of the corresponding intersection point P.
For example, E1' was obtained by moving E1 left for the distance of
D1. P' (e.g., P2') is an intersection point of line L and a
corrected mammary gland edge after serrated artifact was
removed.
Example 6
[0195] FIG. 17 illustrates exemplary mammary gland reconstructed
images. The left portion of FIG. 17 was generated before serrated
artifacts were removed. The right portion of FIG. 17 was generated
after serrated artifacts were removed. As shown in FIG. 17, there
are serrated artifacts along the mammary gland edge in area 1710
before serrated artifacts were removed, and there is no visible
serrated artifact in the mammary gland edge in area 1730 after
serrated artifacts were removed.
Example 7
[0196] FIG. 18 illustrates an exemplary projection image of a
mammary gland. FIG. 18 may be generated by imaging device 110
according to some embodiments of the present disclosure. As shown
in FIG. 18, the mammary gland in area 1810 has a higher gray value
than the right portion of the projection image. The right portion
of the projection image denoted as area 1820 is the background with
a lower gray value than the left portion of the projection
image.
Example 8
[0197] FIG. 19 illustrates a process for generating a segmented
region by merging a plurality of regions of interest according to
some embodiments of the present disclosure. As shown in FIG. 19,
a1, a2, and a3 are three regions of interest, and b is a region
generated by overlaying a1, a2, and a3. C is a segmented region,
which is a union of a1, a2, and a3.
Example 9
[0198] FIG. 20 illustrates a process for generating a segmented
region based on a rectangular segmenting algorithm according to
some embodiments of the present disclosure. As shown in FIG. 19,
the gray area is a region of a mammary gland that is a region of
interest. The segmented region may be rectangle S whose diagonal
vertexes are M (X.sub.1, Y.sub.1) and N (X.sub.2, Y.sub.2). X.sub.1
is the largest horizontal ordinate value of all pixels in a
plurality of regions of interest. Y.sub.1 is the smallest
longitudinal ordinate value of all pixels in the plurality of
regions of interest. X.sub.2 is the smallest horizontal ordinate
value of all pixels in the plurality of regions of interest.
Y.sub.1 is the largest longitudinal ordinate value of all pixels in
the plurality of regions of interest.
Example 10
[0199] FIG. 21 illustrates an exemplary reconstructed image of a
mammary gland. As shown in FIG. 21, there are mammary gland edge
artifacts in area 2110 and area 2130.
[0200] Having thus described the basic concepts, it may be rather
apparent to those skilled in the art after reading this detailed
disclosure that the foregoing detailed disclosure is intended to be
presented by way of example only and is not limiting. Various
alterations, improvements, and modifications may occur and are
intended to those skilled in the art, though not expressly stated
herein. These alterations, improvements, and modifications are
intended to be suggested by this disclosure, and are within the
spirit and scope of the exemplary embodiments of this
disclosure.
[0201] Moreover, certain terminology has been used to describe
embodiments of the present disclosure. For example, the terms "one
embodiment," "an embodiment," and/or "some embodiments" mean that a
particular feature, structure or characteristic described in
connection with the embodiment is included in at least one
embodiment of the present disclosure. Therefore, it is emphasized
and should be appreciated that two or more references to "an
embodiment" or "one embodiment" or "an alternative embodiment" in
various portions of this specification are not necessarily all
referring to the same embodiment. Furthermore, the particular
features, structures or characteristics may be combined as suitable
in one or more embodiments of the present disclosure.
[0202] Further, it will be appreciated by one skilled in the art,
aspects of the present disclosure may be illustrated and described
herein in any of a number of patentable classes or context
including any new and useful process, machine, manufacture, or
composition of matter, or any new and useful improvement thereof.
Accordingly, aspects of the present disclosure may be implemented
entirely hardware, entirely software (including firmware, resident
software, micro-code, etc.) or combining software and hardware
implementation that may all generally be referred to herein as a
"block," "module," "engine," "unit," "component," or "system."
Furthermore, aspects of the present disclosure may take the form of
a computer program product embodied in one or more computer
readable media having computer readable program code embodied
thereon.
[0203] A computer readable signal medium may include a propagated
data signal with computer readable program code embodied therein,
for example, in baseband or as part of a carrier wave. Such a
propagated signal may take any of a variety of forms, including
electro-magnetic, optical, or the like, or any suitable combination
thereof. A computer readable signal medium may be any computer
readable medium that is not a computer readable storage medium and
that may communicate, propagate, or transport a program for use by
or in connection with an instruction execution system, apparatus,
or device. Program code embodied on a computer readable signal
medium may be transmitted using any appropriate medium, including
wireless, wireline, optical fiber cable, RF, or the like, or any
suitable combination of the foregoing.
[0204] Computer program code for carrying out operations for
aspects of the present disclosure may be written in any combination
of one or more programming languages, including an object oriented
programming language such as Java, Scala, Smalltalk, Eiffel, JADE,
Emerald, C++, C#, VB. NET, Python or the like, conventional
procedural programming languages, such as the "C" programming
language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP,
dynamic programming languages such as Python, Ruby and Groovy, or
other programming languages. The program code may execute entirely
on the operator's computer, partly on the operator's computer, as a
stand-alone software package, partly on the operator's computer and
partly on a remote computer or entirely on the remote computer or
server. In the latter scenario, the remote computer may be
connected to the operator's computer through any type of network,
including a local area network (LAN) or a wide area network (WAN),
or the connection may be made to an external computer (for example,
through the Internet using an Internet Service Provider) or in a
cloud computing environment or offered as a service such as a
Software as a Service (SaaS).
[0205] Furthermore, the recited order of processing elements or
sequences, or the use of numbers, letters, or other designations
therefore, is not intended to limit the claimed processes and
methods to any order except as may be specified in the claims.
Although the above disclosure discusses through various examples
what is currently considered to be a variety of useful embodiments
of the disclosure, it is to be understood that such detail is
solely for that purpose, and that the appended claims are not
limited to the disclosed embodiments, but, on the contrary, are
intended to cover modifications and equivalent arrangements that
are within the spirit and scope of the disclosed embodiments. For
example, although the implementation of various components
described above may be embodied in a hardware device, it may also
be implemented as a software only solution--e.g., an installation
on an existing server or mobile device.
[0206] Similarly, it should be appreciated that in the foregoing
description of embodiments of the present disclosure, various
features are sometimes grouped together in a single embodiment,
figure, or description thereof for the purpose of streamlining the
disclosure aiding in the understanding of one or more of the
various inventive embodiments. This method of disclosure, however,
is not to be interpreted as reflecting an intention that the
claimed subject matter requires more features than are expressly
recited in each claim. Rather, inventive embodiments lie in less
than all features of a single foregoing disclosed embodiment.
[0207] In some embodiments, the numbers expressing quantities of
ingredients, properties, and so forth, used to describe and claim
certain embodiments of the application are to be understood as
being modified in some instances by the term "about,"
"approximate," or "substantially." For example, "about,"
"approximate," or "substantially" may indicate .+-.20% variation of
the value it describes, unless otherwise stated. Accordingly, in
some embodiments, the numerical parameters set forth in the written
description and attached claims are approximations that may vary
depending upon the desired properties sought to be obtained by a
particular embodiment. In some embodiments, the numerical
parameters should be construed in light of the number of reported
significant digits and by applying ordinary rounding techniques.
Notwithstanding that the numerical ranges and parameters setting
forth the broad scope of some embodiments of the application are
approximations, the numerical values set forth in the specific
examples are reported as precisely as practicable.
[0208] Each of the patents, patent applications, publications of
patent applications, and other material, such as articles, books,
specifications, publications, documents, things, and/or the like,
referenced herein is hereby incorporated herein by this reference
in its entirety for all purposes, excepting any prosecution file
history associated with same, any of same that is inconsistent with
or in conflict with the present document, or any of same that may
have a limiting affect as to the broadest scope of the claims now
or later associated with the present document. By way of example,
should there be any inconsistency or conflict between the
description, definition, and/or the use of a term associated with
any of the incorporated material and that associated with the
present document, the description, definition, and/or the use of
the term in the present document shall prevail.
[0209] In closing, it is to be understood that the embodiments of
the application disclosed herein are illustrative of the principles
of the embodiments of the application. Other modifications that may
be employed may be within the scope of the application. Thus, by
way of example, but not of limitation, alternative configurations
of the embodiments of the application may be utilized in accordance
with the teachings herein. Accordingly, embodiments of the present
application are not limited to that precisely as shown and
described.
* * * * *