U.S. patent application number 12/129036 was filed with the patent office on 2009-01-01 for object segmentation recognition.
Invention is credited to Peter Dugan, Robert L. Finch, Rosemary D. Paradis, Kenei Suntarat.
Application Number | 20090003651 12/129036 |
Document ID | / |
Family ID | 40088192 |
Filed Date | 2009-01-01 |
United States Patent
Application |
20090003651 |
Kind Code |
A1 |
Dugan; Peter ; et
al. |
January 1, 2009 |
OBJECT SEGMENTATION RECOGNITION
Abstract
A system for segmenting radiographic images of a cargo container
can include an object segmentation recognition module adapted to
perform a series of functions. The functions can include receiving
a plurality of radiographic images of a cargo container, each image
generated using a different energy level and segmenting each of the
radiographic images using one or more segmentation modules to
generate segmentation data representing one or more image segments.
The functions can also include identifying image layers within the
radiographic images using a plurality of layer analysis modules by
providing the plurality of radiographic images and the segmentation
data as input to the layer analysis modules, and determining
adjusted atomic number values for an atomic number image based on
the image layers. The functions can include adjusting the atomic
number image based on the adjusted atomic number values for the
regions of interest to generate an adjusted atomic number image and
identifying regions of interest within the adjusted atomic number
image based on an image characteristic. The functions can also
include providing coordinates of each region of interest and the
adjusted atomic number image as output.
Inventors: |
Dugan; Peter; (Ithaca,
NY) ; Suntarat; Kenei; (Funabashi-shi, JP) ;
Finch; Robert L.; (Endicott, NY) ; Paradis; Rosemary
D.; (Vestal, NY) |
Correspondence
Address: |
MILES & STOCKBRIDGE PC
1751 PINNACLE DRIVE, SUITE 500
MCLEAN
VA
22102-3833
US
|
Family ID: |
40088192 |
Appl. No.: |
12/129036 |
Filed: |
May 29, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60940632 |
May 29, 2007 |
|
|
|
Current U.S.
Class: |
382/103 |
Current CPC
Class: |
G01N 23/06 20130101;
G06K 9/6263 20130101 |
Class at
Publication: |
382/103 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Claims
1. A system for segmenting radiographic images of a cargo
container, the system comprising: an object segmentation
recognition module adapted to perform a series of functions
including: receiving a plurality of radiographic images of a cargo
container, each image generated using a different energy level;
segmenting each of the radiographic images using one or more
segmentation modules to generate segmentation data representing one
or more image segments; identifying image layers within the
radiographic images using a plurality of layer analysis modules by
providing the plurality of radiographic images and the segmentation
data as input to the layer analysis modules, and determining
adjusted atomic number values for an atomic number image based on
the image layers; adjusting the atomic number image based on the
adjusted atomic number values for the regions of interest to
generate an adjusted atomic number image; identifying regions of
interest within the adjusted atomic number image based on an image
characteristic; and providing coordinates of each region of
interest and the adjusted atomic number image as output.
2. The system of claim 1, wherein the plurality of radiographic
images are generated using four energy levels.
3. The system of claim 1, wherein identifying regions of interest
includes comparing an estimated atomic value of each image segment
to a threshold value.
4. The system of claim 1, wherein the plurality of layer analysis
modules include a first layer analysis module and a second layer
analysis module and the function of identifying image layers
includes combining the output of the first layer analysis module
with the second layer analysis module.
5. The system of claim 1, wherein the image characteristic is an
estimated atomic value of a portion of the atomic number image.
6. The system of claim 1, wherein the image characteristic includes
an image segment shape.
7. The system of claim 1, wherein the function of providing
coordinates of each region of interest includes providing the
coordinates of each region of interest and the corrected atomic
number image to an operator station.
8. The system of claim 1, wherein the function of providing
coordinates of each region of interest includes providing the
coordinates of each region of interest and the corrected atomic
number image to another system.
9. A method for segmenting radiographic images comprising:
providing a plurality of radiographic images; providing a pixel
level estimated atomic number image; segmenting the radiographic
images using one or more segmentation modules to generate
segmentation data; identifying image layers within each of the
radiographic images; analyzing the image layers using the plurality
of radiographic images and the segmentation data in order to
determine corrected atomic number values for objects in the images;
correcting the pixel level atomic number image based on the
corrected atomic number values to generate an object level atomic
number image; identifying regions of interest within the object
level atomic number image based on an image characteristic; and
outputting coordinates of each region of interest and the object
level atomic number image as output.
10. The method of claim 9, wherein the plurality of radiographic
images are images of a cargo container.
11. The method of claim 9, wherein the plurality of radiographic
images includes four images each image being generated using a
different energy level.
12. The method of claim 9, wherein identifying regions of interest
includes comparing an estimated atomic value of each image object
to a threshold value.
13. The method of claim 9, wherein the step of identifying image
layers includes using a first layer analysis module and a second
layer analysis module and combining the output of the first layer
analysis module with the second layer analysis module.
14. The method of claim 9, wherein the image characteristic is an
estimated atomic value of a portion of the object level atomic
number image.
15. A radiographic image segmentation apparatus comprising: means
for receiving a radiographic image; means for segmenting each of
the radiographic images using one or more segmentation modules to
generate segmentation data; means for identifying image layers to
produce image layer data; means for identifying regions of interest
based on the segmentation data and image layer data; means for
analyzing the image layers in order to determine corrected atomic
number values for the regions of interest; means for correcting an
atomic number image based on the corrected atomic number values for
the regions of interest; and means for outputting region of
interest coordinates and the corrected atomic number image as
output.
16. The apparatus of claim 15, wherein the radiographic image is an
image of a cargo container.
17. The apparatus of claim 15, wherein the means for receiving a
radiographic image further includes means for receiving a plurality
of radiographic images.
18. The apparatus of claim 17, wherein each radiographic image is
generated using a different energy level.
19. The apparatus of claim 18, wherein the means for analyzing the
image layers includes using the plurality of radiographic images
and the segmentation data.
20. The apparatus of claim 15, wherein the means for identifying
image layers includes first means for identifying image layers and
second means for identifying image layers, the image layer data
being produced using output from the first means for identifying
image layers and output from the second means for identifying image
layers.
Description
[0001] The present application claims the benefit of U.S.
Provisional Patent Application No. 60/940,632, entitled "Threat
Detection System", filed May 29, 2007, which is incorporated herein
by reference in its entirety.
[0002] Embodiments of the present invention relate generally to
image segmentation and, more particularly, to computer systems and
methods for automatic image segmentation of radiographic
images.
[0003] Image segmentation, the process of separating objects of
interest from the background (or from other objects) in an image,
is typically a difficult task for a computer to perform. If an
image scene is simple and the contrast between objects in the scene
and the background is high, then the task may be somewhat easier.
However, if an image scene is cluttered and the contrast between
objects in the scene and the background (or other objects) is low,
image segmentation can be a particularly difficult problem. For
example, in a radiographic image of a three-dimensional object such
as a cargo container there can be numerous layers of objects and
contrast may be low between the objects and the background. In
addition to the difficulties often associated with low contrast and
cluttered scenes, radiographic images of objects having layers may
also present a need to segment the image in two ways: in the x-y
plane (i.e., the plane the image was produced on) and by layer of
depth in order to correct for layer effects such as
overlapping.
[0004] Embodiments of the present invention can be used in an
imaging system, such as a nuclear material detection system, that
includes a capability of producing images using different energy
levels. Each energy level provides a different imaging
characteristic such as energy penetration of the object being
scanned. Different images produced using different energy levels
can be used in conjunction with each other to better identify
layers within the object being scanned.
[0005] Embodiments of the present invention address the above
problems and other problems often associated with segmenting
radiographic images. For example, one exemplary embodiment can
include a system for segmenting radiographic images of a cargo
container. The system can include an object segmentation
recognition module adapted to perform a series of functions. The
functions can include receiving a plurality of radiographic images
of a cargo container, each image generated using a different energy
level and segmenting each of the radiographic images using one or
more segmentation modules to generate segmentation data
representing one or more image segments. The functions can also
include identifying image layers within the radiographic images
using a plurality of layer analysis modules by providing the
plurality of radiographic images and the segmentation data as input
to the layer analysis modules, and determining adjusted atomic
number values for an atomic number image based on the image layers.
The functions can include adjusting the atomic number image based
on the adjusted atomic number values for the regions of interest to
generate an adjusted atomic number image and identifying regions of
interest within the adjusted atomic number image based on an image
characteristic. The functions can also include providing
coordinates of each region of interest and the adjusted atomic
number image as output.
[0006] Another embodiment includes a method for segmenting
radiographic images. The method can include providing a plurality
of radiographic images and providing a pixel level estimated atomic
number image. The method can also include segmenting the
radiographic images using one or more segmentation modules to
generate segmentation data and identifying image layers within each
of the radiographic images. The method can also include analyzing
the image layers using the plurality of radiographic images and the
segmentation data in order to determine corrected atomic number
values for objects in the images, and correcting the pixel level
atomic number image based on the corrected atomic number values to
generate an object level atomic number image. The method can
include identifying regions of interest within the object level
atomic number image based on an image characteristic, and
outputting coordinates of each region of interest and the object
level atomic number image as output.
[0007] Another embodiment includes a radiographic image
segmentation apparatus. The apparatus can include means for
receiving a radiographic image and means for segmenting each of the
radiographic images using one or more segmentation modules to
generate segmentation data. The apparatus can also include means
for identifying image layers to produce image layer data and means
for identifying regions of interest based on the segmentation data
and image layer data. The apparatus can also include means for
analyzing the image layers in order to determine corrected atomic
number values for the regions of interest and means for correcting
an atomic number image based on the corrected atomic number values
for the regions of interest. The apparatus can also include means
for outputting region of interest coordinates and the corrected
atomic number image as output.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 is a block diagram of an exemplary object
segmentation recognition processor showing inputs and outputs;
[0009] FIG. 2 is a block diagram of an exemplary object
segmentation recognition processor showing an exemplary OSR
processor in detail;
[0010] FIG. 3 is a flowchart showing an exemplary method for image
segmentation; and
[0011] FIG. 4 is a block diagram of an exemplary object
segmentation recognition apparatus showing data flow and processing
modules.
DETAILED DESCRIPTION
[0012] FIG. 1 shows a block diagram of an exemplary object
segmentation recognition processor showing inputs and outputs. In
particular, an object segmentation recognition (OSR) processor 102
is shown receiving one or more images 104 as input and providing
region of interest (ROI) or object coordinates 106 as output.
[0013] In operation, the images 104 provided or obtained as input
to the OSR processor 102 can include radiographic images or other
images. For example, the images 104 can include radiographic images
of a cargo conveyance such as a cargo container. The images 104 can
include one or more images, for example four images can be provided
with each image being generated using a different radiographic
energy level. Also, the images 104 can include radiographic images
or other images derived from radiographic images, such as, for
example, an atomic number image representing estimated atomic
numbers associated with radiographic images.
[0014] The OSR processor 102 can obtain, request or receive the
images 104 via a wired or wireless connection, such as a network
(e.g., LAN, WAN, wireless network, Internet or the like) or direct
connection within a system. The OSR processor 102 can also receive
the images 104 via a software connection (e.g., procedure call,
standard object access protocol, remote procedure call, or the
like). In general, any known or later developed wired, wireless or
software connection suitable for transmitting data can be used to
supply the images 104 to the OSR processor 102. The OSR processor
102 can be requested to segment images by another process or
system, or can request images for segmenting from another process
or system. If the images 104 include more than one image, the
images can be registered prior to being sent for segmentation.
[0015] The OSR processor 102 processes the images 104 to segment
the images 104 and identify objects within the images 104. The OSR
processor 102 can also extract or identify layers (or estimated
layers) within the images in order to help segment the images more
accurately. The layer information can also be used to correct or
adjust estimated atomic numbers in an atomic number image or map.
The atomic number image or map can include a representation of
estimated atomic numbers determined from the images 104.
[0016] Once the images 104 have been segmented and the layer
information has been determined, regions of interest (ROIs) within
the images 104 can be located or determined. The ROIs can be
determined based on an image characteristic such as estimated
atomic number of the ROI (or object), shape of the ROI, position or
location of the ROI, or the like. The OSR processor 102 can provide
ROI/object coordinates 106 as output. The ROI/object coordinates
106 can be associated with the input images 104 or an atomic number
image. The output ROI/object coordinates 106 can be outputted via a
wired or wireless connection, such as a network (e.g., LAN, WAN,
Internet or the like) or direct connection within a system. The
output ROI/object coordinates 106 can be outputted via a software
connection (e.g., response to a procedure call, standard object
access protocol, remote procedure call, or the like).
[0017] FIG. 2 is a block diagram of an exemplary object
segmentation recognition processor showing an exemplary OSR
processor in detail. In addition to the components already
described above, the OSR processor 102 includes a segment
processing section 202 having a connected region analysis module
204, an edge analysis module 206, a ratio layer analysis module 208
and a blind source separation (BSS) layer analysis module 210. The
OSR processor 102 also includes an object ROI section 212 having a
layer analysis and segment association module 214 and an object ROI
determination module 216.
[0018] In operation, the segment processing section receives the
images 104. Once received, the images 104 can be processed using
one or more image segmentation modules (e.g., the connected region
analysis module 204, the edge analysis module 206, or a combination
of the above). It will be appreciated that the segmentation modules
shown are for illustration purposes and that any known or later
developed image segmentation processes can be used. Also, the
selection of the number and type of image segmentation modules
employed in the OSR processor 102 may depend on a contemplated use
of an embodiment and the selection may be guided by a number of
factors including, but not limited to, type of materials being
scanned, configuration of the scanning system and objects being
scanned, desired performance characteristics, time available for
processing, or the like. The individual segmentation processes,
layer analysis processes, or both may be performed sequentially or
in parallel or a combination of the above and in any suitable
order.
[0019] Once the segmentation processing (object segmentation, layer
analysis, or both) has been completed, the resulting image segment
data can be provided to the object ROI section 212. In the object
ROI section 212, the layers and segments of the image segment data
are analyzed using layer analysis and segment association module
214 and combined or associated to produce segment-layer data that
contains information about objects and layers within the images
104. The images 104 can be processed by one or more layer analysis
modules (e.g., the ratio layer analysis module 208, the BSS layer
analysis module 210, or a combination of the above) within the
layer analysis and segment association module 214.
[0020] The segment-layer data can be in the form of an atomic
number image that represents a composite of the images 104 and has
been adjusted or corrected based on layers and segments to provide
an image suitable for identification of ROIs. The segment-layer
data can also be represented in any form suitable for transmitting
the information that may be needed to analyze the images 104. The
segment-layer data is then provided to the object ROI determination
module 216 for analysis and identification of ROIs.
[0021] The object ROI determination module 216 can use one or more
image characteristics to identify ROIs within the images 104 or the
segment-layer data. Image characteristics can include an estimated
atomic number for a portion of the image (e.g., a pixel, segment,
object, region or the like), a shape of a segment or object within
the image, or a position or location of an object or segment. In
general, any image characteristic that is suitable for identifying
an ROI can be used.
[0022] Once the ROIs have been determined, coordinate data (106)
representing each ROI can be provided as output. The output can be
provided as described above in connection with reference number 106
of FIG. 1. Also, segment-layer data or an adjusted or corrected
atomic number image can be provided in addition to, or as a
substitute for, the ROI coordinates.
[0023] FIG. 3 is a flowchart showing an exemplary computer
implemented method for image segmentation. Processing begins at
step 302 and continues to step 304.
[0024] In step 304, one or more radiographic images are obtained.
These images can be provided by an imaging system (e.g., an x-ray,
magnetic resonance imaging device, computerized tomography device,
or the like). In general, any imaging device suitable for
generating images that may require segmenting can be used.
Processing continues to step 306.
[0025] In step 306, the radiographic images are segmented. The
segmentation can be performed using one or more image segmentation
processes. Examples of segmentation methods include modules or
processes for segmentation based on clustering, histograms, edge
detection, region growing, level set, graph partitioning,
watershed, model based, and multi-scale. Processing continues to
step 308.
[0026] In step 308, any layers present in the images are
determined. The layers can be determined using one or more layer
extraction or identification processes. For example, a ratio layer
analysis process and a BSS layer analysis process can be used
together to identify layers in the images. For example, using the
layer analysis module 214 mentioned above. A goal of layer
identification and extraction is to remove overlapping effects
which may be present. By removing overlapping effects, the true
gray level of a material can be determined. Using the true gray
level, a material's effective atomic number (and, optionally,
material density) can be determined. Using the effective atomic
number the composition of the material can help in determining
illicit materials, such as special nuclear materials can be
detected automatically.
[0027] The ratio method of layer identification and overlap effect
removal is known in the art as applied to dual energy and is
described in "The Utility of X-ray Dual-energy Transmission and
Scatter Technologies for Illicit Material Detection," a published
Ph.D. Dissertation by Lu Qiang, Virginia Polytechnic Institute and
State University, Blacksburg, Va., 1999, which is incorporated
herein by reference in its entirety. Generally, the ratio method
provides a process whereby a computer can solve for one image layer
and remove any overlapping effects of another layer. Thus, regions
that overlap or may be obscured can be separated into their
constituent layers and a true gray level can be determined for each
layer. The true gray level can be used to more accurately determine
an estimated atomic number an/or material type.
[0028] Blind source separation (or blind signal separation) is a
technique known in the art, and refers generally to the separation
of a set of signals from a set of mixed signals, without the aid of
information (or with very little information) about the source
signals or the mixing process. However, if information about how
the signals were mixed (e.g., a mixing matrix) can be estimated, it
can then be used to determine an un-mixing matrix which can be
applied to separate the components within a mixed signal.
[0029] The BSS method may be limited by the amount of independence
between materials within the mixture. Several techniques exist for
estimating the mixing matrix, some include using an unsupervised
learning process. The process can include incrementally changing
and weighting coefficients of the mixing matrix and evaluating the
mixing matrix until optimal conditions are met. Once the mixing
matrix is estimated, un-mixing coefficients can be computed.
Examples of some BSS techniques include projection pursuit gradient
ascent, projection pursuit stepwise separation, ICA gradient
ascent, and complexity pursuit gradient ascent. In general, an
iterative hill climbing or other type of optimization process can
be used to estimate the mixing matrix and determine an optimal
matrix. Also, contemplated or desired performance levels may
require development of custom algorithms that can be tuned to a
specific empirical terrain provided by the mixing and un-mixing
matrices. Once the layers are identified and overlapping effects
are removed, processing continues to step 310.
[0030] In step 310, segments that have been identified are
associated with any layers that have been determined or identified
in step 308. Associating segments with layers can help to remove
any overlapping effects and also can improve the ability to
determine a true gray value for a segment. Processing continues to
step 312.
[0031] In step 312, ROIs are determined. The ROIs can be determined
based on an image characteristic as described above. Processing
continues to step 314.
[0032] In step 314, a gray level atomic number image is optionally
adjusted to reflect the corrections or adjustments provided by the
layer determination. The adjustments or corrections can include
changes related to removal of overlap effects or other changes.
Processing continues to step 316.
[0033] In step 316, the ROI coordinates and, optionally, the
adjusted or corrected gray level image are provided as output to an
operator or another system. The output can be in a standard format
or in a proprietary format. Processing continues to step 318 where
the method ends. It will be appreciated that steps 304-316 can be
repeated in whole or in part to perform a contemplated image
segmentation process.
[0034] FIG. 4 is a block diagram of an exemplary object
segmentation recognition apparatus showing data flow and processing
modules. In particular, four gray scale radiographic images
(402-408), each generated using a different energy level, are
provided to an effective Z-value determination module 410. The
effective Z-value determination module determines a pixel-level
Z-value gray scale image 412.
[0035] The pixel-level Z-value gray scale image 412 can be provided
to an image segmentation and layer analysis module 414. The
segmentation and layer analysis module 414 segments the image and
analyzes layers, as described above, to generate a layer corrected
image representing true gray values, ROI coordinates, or both.
[0036] It will be appreciated that the modules, processes, systems,
and sections described above can be implemented in hardware,
software, or both. Also, the modules, processes systems, and
sections can be implemented as a single processor or as a
distributed processor. Further, it should be appreciated that the
steps mentioned above may be performed on a single or distributed
processor. Also, the processes, modules, and sub-modules described
in the various figures of the embodiments above may be distributed
across multiple computers or systems or may be co-located in a
single processor or system. Exemplary structural embodiment
alternatives suitable for implementing the modules, sections,
systems, means, or processes described herein are provided
below.
[0037] The modules, processors or systems described above can be
implemented as a programmed general purpose computer, an electronic
device programmed with microcode, a hard-wired analog logic
circuit, software stored on a computer-readable medium or signal, a
programmed kiosk, an optical computing device, a GUI on a display,
a networked system of electronic and/or optical devices, a special
purpose computing device, an integrated circuit device, a
semiconductor chip, and a software module or object stored on a
computer-readable medium or signal, for example.
[0038] Embodiments of the method and system (or their
sub-components or modules), may be implemented on a general-purpose
computer, a special-purpose computer, a programmed microprocessor
or microcontroller and peripheral integrated circuit element, an
ASIC or other integrated circuit, a digital signal processor, a
hardwired electronic or logic circuit such as a discrete element
circuit, a programmed logic circuit such as a PLD, PLA, FPGA, PAL,
or the like. In general, any process capable of implementing the
functions or steps described herein can be used to implement
embodiments of the method, system, or a computer program product
(software program).
[0039] Furthermore, embodiments of the disclosed method, system,
and computer program product may be readily implemented, fully or
partially, in software using, for example, object or
object-oriented software development environments that provide
portable source code that can be used on a variety of computer
platforms. Alternatively, embodiments of the disclosed method,
system, and computer program product can be implemented partially
or fully in hardware using, for example, standard logic circuits or
a VLSI design. Other hardware or software can be used to implement
embodiments depending on the speed and/or efficiency requirements
of the systems, the particular function, and/or particular software
or hardware system, microprocessor, or microcomputer being
utilized. Embodiments of the method, system, and computer program
product can be implemented in hardware and/or software using any
known or later developed systems or structures, devices and/or
software by those of ordinary skill in the applicable art from the
function description provided herein and with a general basic
knowledge of the computer, image processing, radiographic, and/or
threat detection arts.
[0040] Moreover, embodiments of the disclosed method, system, and
computer program product can be implemented in software executed on
a programmed general purpose computer, a special purpose computer,
a microprocessor, or the like. Also, the method of this invention
can be implemented as a program embedded on a personal computer
such as a JAVA.RTM. or CGI script, as a resource residing on a
server or image processing workstation, as a routine embedded in a
dedicated processing system, or the like. The method and system can
also be implemented by physically incorporating the method into a
software and/or hardware system, such as the hardware and software
systems of multi-energy radiographic cargo inspection systems.
[0041] It is, therefore, apparent that there is provided, in
accordance with the present invention, a method, computer system,
and computer software program for image segmentation. While this
invention has been described in conjunction with a number of
embodiments, it is evident that many alternatives, modifications
and variations would be or are apparent to those of ordinary skill
in the applicable arts. Accordingly, Applicant intends to embrace
all such alternatives, modifications, equivalents and variations
that are within the spirit and scope of this invention.
* * * * *