1. Visual Computing Research @ CTI, DePaul University
Daniela Raicu
Assistant Professor

2. 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
Medical Image Processing
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3. 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:
Medical Image Processing
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4. Medical Image Processing
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.
Domain-specific features:
- fingerprints, human faces
visual features
(primitive or low-level image features)
General features:
- color, texture, shape
Drawback:-lack of expressive power
Medical Image Processing
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5. Content-based Image Retrieval
Image Database
Feature
Extraction
Semantic Gap ?
Mountains and water-falls
It is a nice sunset.
Meaning:
Sunset
Text Database
Medical Image Processing
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6. Content-based Image Retrieval
Feature Representation: Two examples of original images and their representations.
Medical Image Processing
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7. Content-based Image Retrieval
Two examples of original images and their representations:
Medical Image Processing
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8. Content-based Image Retrieval
Similarity Measure:
S(q1,t1)
Image T:
Image Q:
, bi = masking bit
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9. Content-based Image Retrieval
Query
Retrieval Results
Medical Image Processing
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10. Content-based Image Retrieval
Image Search
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11. Content-based Image Retrieval
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12. Medical Image Processing
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
backbone
heart
Medical Image Processing
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13. Medical Image Processing
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.
Medical Image Processing
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14. 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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15. Segmentation: Examples
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16. Segmentation: Examples
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17. Texture Analysis & Classification
Organ/Tissue
segmentation 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
Classification rules
for tissue/organs
in CT images
Calculate numerical texture descriptors for each region
[D1, D2,…D21]
Medical Image Processing
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18. Inverse Difference Moment
Medical Imaging
Texture Analysis
Entropy
Energy
Contrast
Homogeneity
SumMean
Variance
Correlation
Maximum Probability
Inverse Difference Moment
Cluster Tendency
.44697
Medical Image Processing
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19. Inverse Difference Moment
Medical Imaging
Texture Analysis
Entropy
Energy
Contrast
Homogeneity
SumMean
Variance
Correlation
Maximum Probability
Inverse Difference Moment
Cluster Tendency
Medical Image Processing
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20. Inverse Difference Moment
Medical Imaging
Texture Analysis
Entropy
Energy
Contrast
Homogeneity
SumMean
Variance
Correlation
Maximum Probability
Inverse Difference Moment
Cluster Tendency
Medical Image Processing
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21. Inverse Difference Moment
Medical Imaging
Texture Analysis
Entropy
Energy
Contrast
Homogeneity
SumMean
Variance
Correlation
Maximum Probability
Inverse Difference Moment
Cluster Tendency
Medical Image Processing
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22. Medical Imaging Texture Analysis Entropy Energy Contrast Homogeneity
SumMean
Variance
Correlation
Maximum Probability
Inverse Difference Moment
Cluster Tendency
Medical Image Processing
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23. Texture Descriptors: Matlab Demo
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24. Organ/Tissue Classification
IF HGRE <= 0.38 AND
ENTROPY > 0.43
AND SRHGE <= 0.20 AND
CONTRAST > 0.029
THEN Prediction = Heart
Probability = 0.99
Classification rules
for tissue/organs
in CT images
Calculate numerical texture descriptors for each region
[D1, D2,…D21]
Algorithm:
- decision trees
Output: Decision Rules
Performance estimated using:
- sensitivity
- specificity
Advantage: Set of decision rules that can be used for annotation
Medical Image Processing
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25. Organ/Tissue Classification
Examples of Decision Tree Rules for Combined Data:
IF (HGRE <= ) & (CLUSTER <= ) & (INVERSE <= ) & (SUMMEAN <= ) & (SRLGE <= ) & (ENEGRY > )
THEN Prediction = Spleen, Probability =
IF (HGRE <= ) & (CLUSTER <= ) & (INVERSE <= ) & (SUMMEAN <= ) & (SRLGE > )
THEN Prediction = Liver , Probability =
IF (HGRE <= ) & (CLUSTER <= ) & (INVERSE <= ) & (SUMMEAN > ) & (GLNU <= )
THEN Prediction = Kidney, Probability =
Medical Image Processing
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26. Organ/Tissue Classification
Examples of Decision Tree Rules for Combined Data:
IF (HGRE <= ) & (CLUSTER > ) & (GLNU > ) & (ENTROPY > ) & (SRHGE <= ) & (CONTRAST > )
THEN Prediction = Heart, Probability =
IF (HGRE <= ) & (CLUSTER > ) & (GLNU <= ) & (LRE <= )
THEN Prediction = Backbone, Probability =
Medical Image Processing
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27. 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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28. Tissue Classification: Matlab Demo
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29. Medical Image Processing
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 , 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 , 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 , 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.
Medical Image Processing
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30. Medical Image Processing
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, (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, (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,
[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.
Medical Image Processing
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31. Intelligent Multimedia Processing Laboratory
Daniela Raicu
Intelligent Multimedia
Processing Laboratory
School of CTI
DePaul University
THE END!
Medical Image Processing
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