Document 7156942
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Visual Computing
Research @ CTI, DePaul University
Daniela Raicu
Assistant Professor
[email protected]
http://facweb.cs.depaul.edu/research/vc
Visual Computing Group
CTI Faculty:
Gian Mario Besana
Lucia Dettori
Jacob Furst
Gerald Gordon
Steve Jost
Yakov Keselman
Daniela Raicu
Collaborators: Department of Radiology, Northwestern
University & Northwestern Memorial Hospital, Chicago, IL
Dr. David Channin, Chief of Informatics, Department of Radiology
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Visual Computing Group
Graduate Students:
John Campion, Ramzy Darwish
William Horsthemke, Gabriel Sanchez, Winnie Tsang
Undergraduate Students:
Stelian Aioanei, Andrew Corboy
Jong Lee, Mikhail Kalinin
Lindsay Semler, Dong-Hui Xu
Visual Computing (VC) area:
CSC381/CSC481: Introduction to Image Processing
CSC382/CSC482: Image Analysis and its Applications
CSC384/CSC484: Introduction to Computer Vision
VC research seminar: Fall Quarter, Friday, 5:00 - 6:00pm
VC workshop: Spring Quarter, Friday, April 15th , 2005
Intelligent Multimedia Processing (IMP) lab:
http://facweb.cs.depaul.edu/research/vc
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Research problems
Content-based Image Retrieval:
Image retrieval systems that permit image searching based
on features automatically extracted from the images’ own
visual content are called content-based image retrieval (CBIR)
systems.
-visual features
(primitive or low-level
image features)
Domain-specific features:
- fingerprints, human faces
General features:
- color, texture, shape
Drawback:-lack of expressive power
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Content-based Image Retrieval
Feature
Extraction
Image Database
Semantic Gap
?
Mountains
and waterfalls It is a nice
sunset.
Text Database
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Meaning:
Sunset
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Content-based Image Retrieval
Feature Representation: Two examples of original images
and their representations.
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Content-based Image Retrieval
Two examples of original images and their representations:
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Content-based Image Retrieval
Similarity Measure:
S(q1,t1)
Image T:
Image Q:
bi S qi ,ti
3* M * N
S Q ,T
i 1
3* M * N
, bi = masking bit
bi
i 1
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Content-based Image Retrieval
Query
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Retrieval Results
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Content-based Image Retrieval
Image Search
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Content-based Image Retrieval
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Medical Imaging
Problem statement: Human body organs’ classifications
using raw data (pixels) from abdominal and chest CT images.
labels for the organs
present in the image
heart
backbone
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Medical Imaging
Segmentation
Organ/Tissue
segmentation in
CT images
- Data: 340 DICOM images
-
Segmented organs: liver (56), kidneys (55), spleen (39), backbone (140),
& heart (50)
Segmentation algorithm: Active Contour Mappings (Snakes)
- A boundary-based segmentation algorithm
- Input for the algorithm: a number of initial points & five main
parameters that influence the way the boundary is formed.
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Segmentation: Matlab Demo
Advantage: it detects complex
shapes
Disadvantage: it needs manual
selection of the initial points
and of the parameters
Our Solution: perform
clustering of similar
regions using a neural
network
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Segmentation: Examples
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Segmentation: Examples
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Texture Analysis &
Classification
Organ/Tissue
segmentation in
CT images
Calculate numerical
texture descriptors
for each region
[D1, D2,…D21]
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Classification rules
for tissue/organs
in CT images
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IF HGRE <= 0.38 AND
ENTROPY > 0.43
AND SRHGE <= 0.20 AND
CONTRAST > 0.029
THEN Prediction = Heart
Probability = 0.99
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Medical Imaging
Texture Analysis
Entropy
Energy
Contrast
3.892828
.034692
2.764427
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Homogeneity SumMean
.6345745
Variance
Correlation
11.662886 7.308909
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.110921
Maximum
Probability
.112929
Inverse
Difference
Moment
Cluster
Tendency
.44697
26.471211
18
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Texture Analysis
Entropy
Energy
Contrast
3.4151415
.108713
6.224426
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Homogeneity SumMean
.631435
Variance
Correlation
Maximum
Probabilit
y
Inverse
Differenc
e Moment
Cluster
Tendenc
y
.0723125
.3081855
.280289
31.139159
13.628323 9.340897
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Medical Imaging
Texture Analysis
Entropy
Energy
Contrast
3.38482
.055998
3.49784
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Homogeneity SumMean
.5577785
Variance
Correlation
Maximum
Probabilit
y
Inverse
Differenc
e Moment
Cluster
Tendenc
y
.1436305
.1250245
.437988
11.453111
14.278469 3.737737
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Medical Imaging
Texture Analysis
Entropy
Energy
Contrast
3.3099875
.049172
3.066407
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Homogeneity SumMean
.5369255
Variance
Correlation
Maximum
Probabilit
y
Inverse
Differenc
e Moment
Cluster
Tendenc
y
.0377875
.0897425
.460422
3.471442
12.309719 1.634463
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Medical Imaging
Texture Analysis
Entropy
Energy
Contrast
Homogeneity
SumMean
Variance
Correlation
Maximum
Probability
Inverse
Difference
Moment
Cluster
Tendency
2.72509
.091388
1.618982
.6208175
11.755226
0.912752
.123976
.1742075
.506894
2.032082
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Texture Descriptors:
Matlab Demo
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Organ/Tissue Classification
Calculate numerical
texture descriptors
for each region
[D1, D2,…D21]
Classification rules
for tissue/organs
in CT images
IF HGRE <= 0.38 AND
ENTROPY > 0.43
AND SRHGE <= 0.20 AND
CONTRAST > 0.029
THEN Prediction = Heart
Probability = 0.99
Algorithm:
- decision trees
Output: Decision Rules
Performance estimated using:
- sensitivity
- specificity
Advantage: Set of decision rules that can be used for
annotation
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Organ/Tissue Classification
Examples of Decision Tree Rules for Combined Data:
• IF (HGRE <= 0.3788) & (CLUSTER <= 0.0383095) & (INVERSE <=
0.768085) & (SUMMEAN <= 0.556015) & (SRLGE <= 0.101655) &
(ENEGRY > 0.106715)
THEN Prediction = Spleen, Probability = 0.928571
• IF (HGRE <= 0.3788) & (CLUSTER <= 0.0383095) & (INVERSE <=
0.768085) & (SUMMEAN <= 0.556015) & (SRLGE > 0.101655)
THEN Prediction = Liver , Probability = 1.000000
• IF (HGRE <= 0.3788) & (CLUSTER <= 0.0383095) & (INVERSE <=
0.768085) & (SUMMEAN > 0.556015) & (GLNU <= 0.087365)
THEN Prediction = Kidney, Probability = 0.924658
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Organ/Tissue Classification
Examples of Decision Tree Rules for Combined Data:
• IF (HGRE <= 0.3788) & (CLUSTER > 0.0383095) & (GLNU > 0.03184) &
(ENTROPY > 0.433185) & (SRHGE <= 0.19935) & (CONTRAST >
0.0295805)
THEN Prediction = Heart, Probability = 0.988372
• IF (HGRE <= 0.3788) & (CLUSTER > 0.0383095) & (GLNU <= 0.03184)
& (LRE <= 0.123405)
THEN Prediction = Backbone, Probability = 1.000000
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Organ/Tissue Classification
Decision Tree Accuracy on Testing Data
(Co-occurrence, Run-length, and Combined):
ORGAN
Sensitivity
Specificity
Precision
Accuracy
Backbone
96% / 98% / 98%
99% / 100% / 99%
99% / 99% / 99%
98% / 99% / 99%
Liver
64% / 57% / 78%
96% / 98% / 95%
75% / 84% / 71%
92% / 92% / 92%
Heart
79% / 82% / 75%
96% / 95% / 98%
80% / 77% / 90%
94% / 93% / 95%
Kidney
89% / 89% / 89%
96% / 93% / 96%
80% / 67% / 77%
94% / 92% / 95%
Spleen
60% / 44% / 60%
93% / 93% / 95%
53% / 45% / 63%
89% / 87% / 91%
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Tissue Classification:
Matlab Demo
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Publications (CBIR)
[1] Daniela Stan and Ishwar K. Sethi, “Image Retrieval using a Hierarchy of Clusters” in Lecture
Notes in Computer Science: Advances in Pattern Recognition – ICAPR 2001, Springer-Verlag
Ltd. (Ed), pp. 377-388, 2001.
[2] Daniela Stan and Ishwar K. Sethi, “Mapping Low-level Image Features to Semantic
Concepts” in Proceedings of SPIE: Storage and Retrieval for Media databases, pp. 172-179,
2001.
[3] Ishwar K. Sethi, Ioana Coman, Daniela Stan, “Mining Association Rules between Low-level
Image Features and High-level Concepts” in Proceedings of SPIE: Data Mining and Knowledge
Discovery III, pp.279-290, 2001.
[4] Daniela Stan and Ishwar K. Sethi, “Color Patterns for Pictorial Content Description”, ACM
Symposium on Applied Computing, 2002.
[5] Daniela Stan and Ishwar K. Sethi, “eID: A System for Exploration of Image Databases”,
Information Processing and Management Journal,2002.
[6] Daniela Stan and Ishwar K. Sethi, “Synobins: An intermediate level towards Annotation
and Semantic Retrieval”, IEEE Trans. Multimedia Journal.
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Publications (MI)
[1] D. Xu, J. Lee, D.S. Raicu, J.D. Furst, D. Channin. "Texture Classification of Normal Tissues
in Computed Tomography", The 2005 Annual Meeting of the Society for Computer
Applications in Radiology, June 2-5, 2005. (Submitted)
[2] D.S. Raicu, W. Tsang, M. Kalinin, D. Xu, J.D. Furst, D. Channin. "Automatic Tissue Context
Determination in Computed Tomography", SPIE Medical Imaging, February 12–17, 2005.
(Submitted)
[3] D. H. Xu, A. Kurani, J. D. Furst, & D. S. Raicu, "Run-length encoding for volumetric
texture", The 4th IASTED International Conference on Visualization, Imaging, and Image
Processing - VIIP 2004, Marbella, Spain, September 6-8, 2004.
[4] D. Channin, D. S. Raicu, J. D. Furst, D. H. Xu, L. Lilly, C. Limpsangsri, "Classification of
Tissues in Computed Tomography using Decision Trees", Poster and Demo, The 90th
Scientific Assembly and Annual Meeting of Radiology Society of North America (RSNA04),
November 28, 2004.
[5] A. Kurani, D. H. Xu, J. D. Furst, & D. S. Raicu, "Co-occurrence matrices for volumetric
data", The 7th IASTED International Conference on Computer Graphics and Imaging –
CGIM, August 16-18, 2004 .
[6] D. S. Raicu, J. D. Furst, D. Channin, D. H. Xu, & A. Kurani, "A Texture Dictionary for Human
Organs Tissues' Classification", Proceedings of the 8th World Multiconference on
Systemics, Cybernetics and Informatics (SCI 2004), July 18-21, 2004.
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Daniela Raicu
Intelligent Multimedia
Processing Laboratory
School of CTI
DePaul University
THE END!
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