U.S. patent application number 10/097096 was filed with the patent office on 2002-11-28 for method and system for detecting colorimetric abnormalities in vivo.
Invention is credited to Adler, Doron, Glukhovsky, Arkady, Levy, Daphna, Zinaty, Ofra.
Application Number | 20020177779 10/097096 |
Document ID | / |
Family ID | 23052498 |
Filed Date | 2002-11-28 |
United States Patent
Application |
20020177779 |
Kind Code |
A1 |
Adler, Doron ; et
al. |
November 28, 2002 |
Method and system for detecting colorimetric abnormalities in
vivo
Abstract
A system and method for detection of colorimetric abnormalities
within a body lumen includes an image receiver for receiving images
from within the body lumen. Also included are a transmitter for
transmitting the images to a receiver, and a processor for
generating a probability indication of presence of colorimetric
abnormalities on comparison of color content of the images and at
(east one reference value.
Inventors: |
Adler, Doron; (Nesher,
IL) ; Zinaty, Ofra; (Haifa, IL) ; Levy,
Daphna; (Carmiel, IL) ; Glukhovsky, Arkady;
(Nesher, IL) |
Correspondence
Address: |
Eitan, Pearl, Latzer & Cohen-Zedek
One Crystal Park
Suite 210
2011 Crystal Drive
Arlington
VA
22202-3709
US
|
Family ID: |
23052498 |
Appl. No.: |
10/097096 |
Filed: |
March 14, 2002 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60275486 |
Mar 14, 2001 |
|
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|
Current U.S.
Class: |
600/476 ;
128/923; 348/77 |
Current CPC
Class: |
G06T 2207/30028
20130101; A61B 1/00009 20130101; G06T 2207/10024 20130101; A61B
5/0031 20130101; A61B 5/02042 20130101; A61B 5/0084 20130101; A61B
1/00045 20130101; G06T 7/90 20170101; G06T 7/0012 20130101; A61B
5/0075 20130101; A61B 1/000094 20220201; G06T 2207/10068 20130101;
A61B 5/743 20130101; A61B 5/742 20130101; A61B 1/041 20130101; A61B
5/073 20130101 |
Class at
Publication: |
600/476 ; 348/77;
128/923 |
International
Class: |
A61B 005/00 |
Claims
What is claimed is:
1. A method for detecting abnormalities in-vivo, the method
comprising the steps of: analyzing an image for at least one
spectral characteristic; and creating an abnormality determination
based on the analysis.
2. The method of claim 1, wherein the abnormality determination is
a determination of the presence of an abnormality.
3. The method of claim 1, wherein the abnormality determination is
a probability determination of the presence of an abnormality.
4. The method of claim 1, wherein analyzing the image includes at
least analyzing the image for at least one spectral characteristic
in relation to a reference value.
5. The method of claim 1, wherein the image is of a body lumen.
6. A method as in claim 1, wherein said spectral characteristics
include hue values.
7. A method as in claim 1, wherein said at least one reference
value includes a pathology sample.
8. A method as in claim 7, wherein said pathology sample includes
blood.
9. A method as in claim 1, wherein said abnormality is due to the
presence of blood.
10. A method as in claim 1, wherein said analysis includes
comparing images.
11. A method as in claim 1 wherein the image is received from an
in-vivo camera imager.
12. A method as in claim 1 wherein the image is received from a
capsule.
13. A method according to claim 1, further comprising deterimining
the position of the image.
14. A system for detecting abnormalities in-vivo, the system
comprising: an image receiver means for receiving images; an
analysis means for analyzing an image for at least one spectral
characteristic and for making an abnormality determination based on
the analysis.
15. A system for detecting abnormalities in-vivo, the system
comprising: an image receiver module capable of receiving images;
an analysis module capable of analyzing an image for at least one
spectral characteristic and making an abnormality determination
based on the analysis.
16. The system of claim 15 wherein the abnormality determination is
a probability.
17. The system of claim 15, wherein analyzing the image includes at
least analyzing the image for at least one spectral characteristic
in relation to a reference value.
18. The system of claim 15, wherein the image is of a body
lumen.
19. The system as in claim 17, wherein said at least one reference
value includes a healthy tissue reference value.
20. The system as in claim 17, wherein said at least one reference
value includes a pathology sample.
21. The system as in claim 20, wherein said pathology sample
includes blood.
22. The system as in claim 15, wherein said abnormality is due to
the presence of blood.
23. The system as in claim 15, wherein said analysis includes
comparing images.
24. The system as in claim 15 wherein the image is received from an
in-vivo camera imager.
25. The system as in claim 15 wherein the image is received from a
capsule.
26. A method for calculation of a reference value for tissue, the
method comprising the steps of: receiving at least a first image
and a second image from within a body lumen; selecting blocks of
pixels within said images based on colorimetric parameters;
averaging said colorimetric parameters of said selected blocks of
pixels of said at least first and second images; and filtering said
calorimetric parameters, thereby obtaining a reference value for
tissue.
27. A method as in claim 26, wherein said step of receiving
includes receiving multiple images.
28. A method as in claim 26, wherein said colorimetric parameters
include hue.
29. A method as in claim 26, wherein said colorimetric parameters
include brightness.
30. A method as in claim 26, wherein said tissue is healthy
tissue.
31. A swallowable capsule for detecting colorimetric abnormalities
in a gastrointestinal tract, the capsule comprising: an
image-receiver for receiving images from said gastrointestinal
tract; and a processor for generating a probability indication for
presence of colorimetric abnormalities by comparing color content
of said images to at least one reference value.
32. A capsule as in claim 31, wherein said at least one reference
value is a pathology sample.
33. A capsule as in claim 32 wherein said pathology sample includes
blood.
34. A capsule as in claim 31, wherein said at least one reference
value includes a healthy tissue reference sample.
35. A capsule as in claim 31, wherein said at least one reference
value includes a pathology sample.
36. A capsule as in claim 31, wherein said color content includes
spectral characteristics.
37. A capsule as in claim 36, wherein said spectral characteristics
include hue.
38. A capsule as in claim 36, wherein said spectral characteristics
include saturation.
39. A capsule as in claim 36 wherein said spectral characteristics
include brightness.
40. An apparatus for determining calorimetric abnormalities within
a body lumen, said apparatus comprising: an image-receiver capable
of receiving images from a body lumen; a spectral analyzer capable
of determining color content of said images; and a processor
capable of generating a probability indication for presence of an
abnormal condition by comparing said color content to at least one
reference value.
41. An apparatus as in claim 40 wherein said processor is
configured for real-time processing.
42. An apparatus as in claim 40, wherein said processor is
configured for post-processing.
43. An apparatus as in claim 40, wherein said pathological
condition includes bleeding
44. An apparatus as in claim 40, wherein said at least one
reference value includes a value for healthy tissue.
45. An apparatus as in claim 40, wherein said at least one
reference value includes a value for pathological tissue.
46. A system for detection of a calorimetric abnormality within the
gastrointestinal tract, said system comprising: a swallowable
capsule having an in-vivo imager for obtaining images from within
said body lumen; a transmitter capable of transmitting said images
to a receiver; and a processor capable of generating a probability
indication of presence of blood based on comparison of color
content of said received images and at least one reference
value.
47. A system according to claim 36 wherein said colorimetric
abnormality indicates a pathological condition.
48. A system according to claim 37 wherein said pathological
condition includes bleeding.
49. A system according to claim 36 wherein said at least one
reference value includes a value for healthy tissue.
50. A system according to claim 36 wherein said at least one
reference value includes a value for pathological tissue.
51. A system according to claim 36, further comprising a position
indicators thereby allowing visualization of a location of said
results within said body lumen.
52. A method for detecting abnormalities in-vivo, the method
comprising the steps of: analyzing an image stream for at least one
spectral characteristic; creating an abnormality determination
based on the analysis; and presenting to a user the abnormality
determination.
53. The method of claim 52, wherein the abnormality determination
is a determination of the probabiltiy of an abnormality.
54. The method according to claim 1, wherein the image is part of
an image stream.
55. An apparatus for determining colorimetric abnormalities
in-vivo, said apparatus comprising: an image-receiver capable of
receiving images; and a processor capable of analyzing color
content of said images, generating a an abnormality indication for
the presence of an abnormal condition and presenting to a user an
abnormality diagnosis.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to a method and system for
detection of colorimetric abnormalities in vivo, and specifically
within the gastrointestinal (GI) tract.
BACKGROUND OF THE INVENTION
[0002] Pathologies of the gastrointestinal (GI) tract may exist for
a variety of reasons. Some examples of pathologies include
bleeding, lesions, angiodisplasia, Crohn's disease, polyps, celiac
disorders, and others. The majority of pathologies result in
changes of color and/or texture of the inner surface of the GI
tract.
[0003] As one example, color changes may be due to bleeding. Blood
may be present within the digestive tract for a variety of
pathological reasons, including ulcers, cancer, or other disease
conditions. It is often difficult to detect the presence of blood
within the GI tract, since bleeding can occur in difficult to reach
locations. In addition, it is difficult to "see" inside the tract,
especially in sections which are hard to reach such as the small
intestines.
[0004] Several approaches have been used to try to detect the
presence of blood within the GI tract. One approach has been the
detection of blood in the feces by visual and/or chemical means.
The main drawback of this approach has been that the concentration
of blood in the feces is lower than the concentration of blood at
the bleeding site, since additional materials are accumulated along
the GI passage. Therefore, the sensitivity of this approach is low.
In addition, the specific bleeding site along the GI tract cannot
be determined.
[0005] A second, more invasive technique, has been the use of an
endoscope or enteroscope. This approach enables direct
visualization of parts of the GI tract. However, most portions of
the small intestine are inaccessible by this method.
[0006] Other examples of pathologies which may be detected based on
the red part of the spectrum include active bleeding, blood clots,
polyps, lesions, ulcerations, angiodisplasia and telangectasia.
Pathologies which may be characterized by blue/violet color include
arterio-venous malformation (AVM) and submucosal bleeding. AVM may
also appear in red. In addition, some types of ulcers are
characterized by white color.
SUMMARY OF THE INVENTION
[0007] There is provided, in accordance with one embodiment of the
present invention a method for detecting colorimetric abnormalities
in a body lumen. The method includes the step of calculating a
probability indication of a presence of an abnormal color within
the body lumen based on comparison of spectral characteristics to
at least one reference value.
[0008] There is provided, in accordance with another embodiment of
the present invention, a method for calculation of a reference
value for tissue. The method includes the steps of receiving at
least a first image and a second image from within a body lumen,
selecting blocks of pixels within the images based on calorimetric
parameters, averaging the colorimetric parameters of the selected
blocks of pixels of the first and second images, and filtering the
calorimetric parameters, thereby obtaining a reference value for
tissue.
[0009] There is provided, in accordance with another embodiment of
the present invention, a swallowable capsule for detecting
colorimetric abnormalities in a gastrointestinal tract. The capsule
includes an image-receiver for receiving images from the
gastrointestinal tract, and a processor for generating a
probability indication for presence of calorimetric abnormalities
by comparing color content of the images to at least one reference
value.
[0010] There is provided, in accordance with another embodiment of
the present invention, an apparatus for determining colorimetric
abnormalities within a body lumen. The apparatus includes an
image-receiver for receiving images from a body lumen, a spectral
analyzer for determining color content of the images, and a
processor for generating a probability indication for presence of
an abnormal condition by comparing the color content to at least
one reference value.
[0011] There is provided, in accordance with another embodiment of
the present invention, a system for detection of blood within a
body lumen. The system includes a swallowable capsule having an
in-vivo imager for obtaining images from within the body lumen, a
transmitter for transmitting the images to a receiver, and a
processor for generating a probability indication of presence of
blood based on comparison of color content of the received images
and at least one reference value.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The present invention will be understood and appreciated
more fully from the following detailed description taken in
conjunction with the drawings in which:
[0013] FIG. 1 is a schematic illustration of a prior art in vivo
camera system;
[0014] FIG. 2 is a schematic illustration of the classification of
samples according to their spectral components;
[0015] FIG. 3 is a block diagram illustration of a system according
to one embodiment of the present invention;
[0016] FIG. 4 is a flow chart illustration of the method used by
the system shown in FIG. 3; and
[0017] FIG. 5 is a schematic illustration of adaptive building of a
reference tissue sample.
DETAILED DESCRIPTION OF THE INVENTION
[0018] The present invention relates to a method and system of
detection of pathologies by spectral analysis of images captured by
a moving in-vivo video camera system. This analysis is based on
detection of colorimetric abnormalities, or deviations from an
expected spectrum. The in-vivo video camera system may be included
on an endoscope, a swallowable capsule, or any other device which
is introduced into the body to view the interior.
[0019] U.S. Pat. No. 5,604,531, assigned to the common assignee of
the present application and incorporated herein by reference,
teaches an in vivo camera system, which is carried by a swallowable
capsule. The in vivo video camera system captures and transmits
images of the GI tract while the capsule passes through the GI
lumen. In addition to the camera system, the capsule contains an
optical system for imaging an area of interest onto the camera
system and a transmitter for transmitting the video output of the
camera. The capsule can pass through the entire digestive tract and
operate as an autonomous video endoscope. It images even the
difficult to reach areas of the small intestine.
[0020] Reference is made to FIG. 1, which shows a schematic diagram
of the system, described in U.S. Pat. No. 5,604,531. The system
comprises a capsule 40 having an imager 46, an illumination source
42, and a transmitter 41. Outside the patient's body are an image
receiver 12 (usually an antenna array), a storage unit 19, a data
processor 14, an image monitor 18, and a position monitor 16. While
FIG. 1 shows separate monitors, both an image and its position can
be presented on a single monitor.
[0021] Imager 46 in capsule 40 is connected to transmitter 41 also
located in capsule 40. Transmitter 41 transmits images to image
receiver 12, which sends the data to data processor 14 and to
storage unit 19. Data processor 14 analyzes the data and is in
communication with storage unit 19, transferring frame data to and
from storage unit 19. Data processor 14 also provides the analyzed
data to image monitor 18 and position monitor 16 where the
physician views the data. The image monitor presents an image of
the GI lumen and the position monitor presents the position in the
GI tract at which the image was taken. Data processor 14 can be
configured for real time processing or for post processing to be
viewed at a later date. In addition to revealing pathological
conditions of the GI tract, the system can provide information
about the location of these pathologies.
[0022] In a preferred embodiment of the present invention, received
images are analyzed for color content. Based on this analysis, as
described hereinbelow, determination as to the presence or absence
of a colorimetric abnormality may be made. A colorimetric
abnormality may indicate a pathological condition, such as
bleeding. Other examples of pathologies which may be detected based
on the red part of the spectrum include active bleeding, blood
clots, polyps, lesions, ulcerations, angiodisplasia and
telangectasia. Pathologies which may be characterized by
blue/violet color include arterio-venous malformation (AVM) and
submucosal bleeding. AVM may also appear in red. In addition, some
types of ulcers are characterized by white color. It will be
apparent that the method and system described hereinbelow may be
useful in detecting any colorimetric deviation from the normal
color content of a body lumen, whether or not a pathological
condition is present.
[0023] Reference is now made to FIG. 2, which is a schematic
illustration of the classification of samples according to their
spectral components. Each test sample T is located within a
coordinate system represented by the following variables: hue H,
saturation S and value V. Hue H represents a number related to the
dominant wavelength of the color stimulus, and varies from 0 to 1
as the color changes from red to yellow to green to cyan to blue to
magenta and back to red again. Saturation S corresponds to color
purity, and in the case of a pure color is equal to 100%. Value V
is a measure of relative intensity of color, representing
brightness of red, blue and green (RBG). A distance vector r(B,T)
between test sample T and an ideal pathology sample B is
calculated. Another distance vector r(R,T) between test sample T
and a reference sample of healthy tissue R is calculated. The
relationship of distance vector r(B,T) and distance vector r(R,T)
is calculated. Each test sample T is classified based on the
relationship between distance vector r(B,T) and distance vector
r(R,T). Briefly, if distance vector r(B,T) is small relative to
distance vector r(R,T), there is a positive indication of
pathological color. In the preferred embodiment, the analysis is
set up to include a higher possibility of false positives than
false negatives, so as to minimize the likelihood of missing a
positive diagnosis. However, other embodiments of analysis are
possible as well.
[0024] Reference is now made to FIGS. 3 and 4, which illustrate a
system 15 and a flow chart diagram showing the steps of using
system 15 for determining the blood content or any other
color-distinguishable pathology within the gut. System 15 comprises
illumination source 42', image receiver 12', data processor 14',
and image monitor 18'. Data processor 14' comprises a spectral
analyzer 22, an adaptive reference builder 24, a distance
calculator 26, and a decision calculator 28. According to one
embodiment of the invention, data processor 14' is a standard
computer accelerator board, high performance computer,
multiprocessor or any other serial or parallel high performance
processing machine. Image monitor 18' may be a video display, or a
graph, table or any other indicator.
[0025] Steps of FIG. 4 may be accomplished using system 15 of FIG.
3. In one embodiment, images are captured and processed within a
capsule. In another embodiment, images are captured by an in-vivo
system, and are transmitted to a remote location where they are
processed. Image receiver 12' receives (step 101) images captured
by the in-vivo camera system of FIG. 1 or any other in-vivo imager.
Data processor 14' divides (step 102) the color images into a grid
of pixels. As in other imaging applications, the number of pixels
determines the resolution of the image. For purposes of this
discussion, the images are divided into blocks (i,j) of 8.times.8
pixels. Since, in one embodiment, the original image is a
256.times.256 pixel image, the result of dividing into 8 pixels,
and determining the color components is a 32.times.32.times.3
matrix of color component value blocks. Spectral analyzer 22
calculates (step 104) the color components of each block: hue
H.sub.i,j; saturation S.sub.i,j; and brightness value V.sub.i,j for
each image.
[0026] Spectral analyzer also calculates (steps 105 and 106-110)
the color components of blocks of pathology sample B and of healthy
reference tissue R. Spectral analyzer 22 calculates (step 105) the
color components of blocks of pathology sample B from known images
containing blood.
[0027] Reference is now made to FIG. 5, which is a schematic
illustration of the adaptive reference building steps 106-110 of
FIG. 4. Adaptive reference builder 24 calculates (steps 106-110)
tissue reference color components in order to build a reference
sample of healthy tissue. The adaptive approach is based on
averaging healthy tissue appearing in subsequent images. Averages
are used since the parameters of healthy tissue along the GI tract
may change. Adaptive reference builder 24 selects (step 107) blocks
based on value V (brightness) and hue H. In one embodiment, the
conditions are: 0.1<V.sub.i,j<0.9 and 0<H.sub.i,j<0.09.
These conditions indicate that healthy tissue is present. As shown
in FIG. 5, images P.sub.i, P.sub.i-1, and P.sub.i-2 with regions
R.sub.i, R.sub.i-1, and R.sub.i-2 of healthy tissue are obtained.
Adaptive reference builder 24 averages (step 108) color components
of healthy regions R.sub.i, R.sub.i-1, and R.sub.i-2 (i.e. the
selected blocks) of images P.sub.i, P.sub.i-1, and P.sub.i-2
obtained along the GI tract. To smooth the data and eliminate
sensitivity to particular images, adaptive reference builder 24
filters (step 110) the average tissue colors of the present image
P.sub.i and the previous image P.sub.i-1.
[0028] In one embodiment an Infinite Impulse Response (IIR) filter
with the following iterative computation is used:
out(t.sub.i)=0.08*in(t.sub.i)+0.92*out(t.sub.i-1)
[0029] where t.sub.i represents the time index of the current frame
i and t.sub.i-1 represents the time index of the previous frame
i-1.
[0030] Referring back to FIG. 4, distance calculator 26 then
calculates (step 112) the Euclidian distance between each block in
the matrix and blood reference value B. Blood reference value B is
obtained from known images containing blood, analyzed by spectral
analyzer 22 as described above. In another embodiment, a different
colorimetric reference value may be used for indication of other
unusual colors. The result of this calculation, for the exemplary
embodiment, is a matrix of 32.times.32 elements P.beta..sub.i,j.
This calculation is done according to the following equation: 1 i ,
j = ( H i , j - H b ) 2 + ( S i , j - S b ) 2 + ( V i , j - V b ) 2
( H b 2 + S b 2 + V b 2 ) * ( H i , j 2 + S i , j 2 + V i , j 2
)
[0031] where H.sub.b, S.sub.b and V.sub.b are the reference values
for hue, saturation and brightness, respectively of blood.
[0032] A similar distance calculation is calculated relative to the
adaptive tissue reference color (healthy tissue) components,
resulting in a 32.times.32 matrix I.sub.i,j as follows. 2 i , j = (
H i , j - H t ) 2 + ( S i , j - S t ) 2 + ( V i , j - V t ) 2 ( H t
2 + S t 2 + V t 2 ) * ( H i , j 2 + S i , j 2 + V i , j 2 )
[0033] where H.sub.t, S.sub.t and V.sub.t are the reference values
for hue saturation and brightness, respectively, of healthy
tissue.
[0034] Once the distance matrices are obtained, decision calculator
28 calculates (step 116) a probability indication function A
according to the following equation: 3 = i , j { ( i , j
BloodThreshold ) ( i , j t , j TissueRationThreshold ) }
[0035] The threshold can be set to any value. In a preferred
embodiment, the threshold values are as follows:
BloodThreshold=0.15 and TissueRatioThreshold=4. Blood exists if
.LAMBDA.>0.
[0036] Finally, image monitor 18' displays (step 118) the results,
either as a color video showing the presence of bloods or as a
graph or table indicating the levels and/or threshold values.
[0037] Display of results may include incorporation of a position
indicator, so that the end user can determine where the presence of
color change is within the GI tract, or other body lumen. Thus, the
physician will be able to deal with the problem area.
[0038] It will be appreciated by persons skilled in the art that
the present invention is not limited to what has been particularly
shown and described hereinabove. Rather the scope of the present
invention is defined only by the claims that follow:
* * * * *