1. Texture Classification of Normal Tissues in Computed Tomography
1Dong-Hui Xu, J. Lee, Daniela S. Raicu, J.D. Furst & 2David S. Channin
1Intelligent Multimedia Processing Laboratory,
School of Computer Science, Telecommunications, Information Systems,
DePaul University, Chicago, USA
2Department of Radiology,
Northwestern University Medical School, Chicago, USA 

2. Motivation
This research will demonstrate how co-occurrence and run-length texture information from computed tomography (CT) images can be used to automatically classify and annotate normal tissues from regions of interest of heart and great vessels, liver, renal and splenic parenchyma.
Automatic classification and annotation of these images will save radiologists time and assist them in processing large volumes of patient data.
9/19/2018
D. Raicu, SCAR 2005

3. Output: Classification
System Diagram
Input: DICOM images of
Computed Tomography
studies for chest &
abdomen
Output: Classification
rules for heart, renal,
splenic parenchyma,
liver, and backbone
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D. Raicu, SCAR 2005

4. Segmentation Segmentation
Data: 340 DICOM images
Segmented organs:
liver, renal, splenic parenchyma, backbone, & heart
Segmentation algorithm: Active Contour Mappings (Snakes)
A boundary-based segmentation
algorithm with the following
inputs:
a set of initial points
five main parameters
that influence the way
the boundary is formed
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D. Raicu, SCAR 2005
Segmentation
Segmentation

5. Segmentation
The values of the five parameters simulate the action of two forces:
Internal: designed to keep the snake smooth during the
deformation
External: designed to move the snake towards the boundary
Output for the algorithm:
The curve evolves to match
the nearest internal boundary,
typically based on gradient
intensity measures.
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D. Raicu, SCAR 2005

6. Segmentation: Heart
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D. Raicu, SCAR 2005

7. Texture Models What is texture?
Texture is a measure of the variation of the intensity of a surface, quantifying properties such as smoothness, coarseness, and regularity.
Texture is a connected set of pixels satisfying a given gray level property which occurs repeatedly in an image region.
9/19/2018
D. Raicu, SCAR 2005

8. Texture Models Texture Models:
Co-occurrence Matrix: the model captures the spatial dependence of gray-level values within an image.
Texture features: entropy, variance, energy, correlation, contrast, maximum probability, homogeneity, inverse difference moment, SumMean, cluster tendency
Run-Length Encoding Matrix: the model the coarseness of the texture in a specific direction.
Texture features: short run emphasis (SRE) , long run emphasis (LRE), high gray-level run emphasis (HGRE), low gray-level run emphasis (LGRE), run percentage (RPC)
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D. Raicu, SCAR 2005

9. Texture Feature Extraction
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D. Raicu, SCAR 2005

10. Organ/Tissue Classification
IF HGRE <= 0.38
AND CLUSTEND <= 0.048
AND INVDIFFM > 0.74
AND LRHGE > 0.46
THEN Prediction = 'Liver' Probability = 1.00
Classification rules
for tissue/organs
in CT images
Calculate numerical texture descriptors for each region
[D1, D2,…D21]
Algorithm:
CART Decision Tree
Output:
Decision Rules
Advantages:
Automatic & efficient processing for:
Classification
Annotation
Good to excellent predictive accuracy
9/19/2018
D. Raicu, SCAR 2005

11. Organ/Tissue Classification
Specifications
Dataset: 66% used for training, 34% reserved for testing
CART algorithm
Cross-validation folds = 10
Maximum Tree Depth = 20
Parent Node/Child Node = 28/5
Minimum Change in Impurity =
Impurity Measure = Gini
Resulting Tree
Total number of nodes 41
Total number of levels 8
Total number of terminal nodes 21
Resulting Rules
Total number of rules: 21 (heart (3), kidneys (3), spleen (5),
liver (8), and backbone (2)
9/19/2018
D. Raicu, SCAR 2005

12. Examples of Decision Tree Rules
IF (HGRE <= 0.38) & (CLUSTEND <= 0.05) & (INVDIFFM <= 0.74) & (SUMMEAN > 0.56) & (RLNU > 0.02)
THEN Prediction = ‘Renal', Probability = 0.94
IF (HGRE <= 0.38) & (CLUSTEND > 0.05) & (SRHGE <= 0.19) & (ENTROPY <= 0.51) & (LRLGE > 0.16)
THEN Prediction = 'Liver', Probability = 1.00
IF (HGRE <= 0.38) & (CLUSTEND > 0.05) & (SRHGE <= 0.19) & (ENTROPY > 0.51) & (GLNU > 0.02)
THEN Prediction = 'Heart', Probability = 0.96
9/19/2018
D. Raicu, SCAR 2005

13. Most Significant Features
The most important determining features for classification are located in the nodes at the top of the classification tree.
HGRE (High Gray Level Run-Emphasis)
CLUSTEND (Cluster Tendency)
HOMOGENE (Homogeneity)
INVDIFFM (Inverse Difference Moment)
SRHGE (Short Run High Gray Level Emphasis)
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D. Raicu, SCAR 2005

14. Classification Results
Training Data
ORGAN
Sensitivity
Specificity
Precision
Accuracy
Backbone
99.7%
99.5%
99.2%
99.6%
Liver
80.0%
96.9%
83.8%
94.1%
Heart
84.6%
98.5%
90.6%
96.5%
Renal
92.7%
97.9%
89.7%
97.1%
Splenic parenchyma
79.5%
96.1%
73.6%
9/19/2018
D. Raicu, SCAR 2005

15. Classification Results
Testing Data
ORGAN
Sensitivity
Specificity
Precision
Accuracy
Backbone
100%
97.6%
96.8%
98.6%
Liver
73.8%
95.9%
76.2%
92.5%
Heart
73.6%
97.2%
84.1%
93.2%
Renal
86.2%
97.8%
87.5%
96.0%
Splenic parenchyma
70.5%
95.1%
62.0%
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D. Raicu, SCAR 2005

16. Summary
The results show that using only 21 texture descriptors calculated from Hounsfield unit data, it is possible to automatically classify regions of interest representing different organs or tissues in CT images.
Furthermore, the results lead us to the conclusion that the incorporation of some other texture models into our proposed approach will increase the performance of the classifier, and will also extend the classification functionality to other organs.
9/19/2018
D. Raicu, SCAR 2005

17. Demo: HEART OPEN: To open a new Image. SEGMENT:
Automatic segmentation of the regions of interest
TEXTURE:
Automatic calculation of the texture descriptors
CLASSIFICATION:
Automatic classification of the segmented regions
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D. Raicu, SCAR 2005

18. HEART: Segmentation The application allows users to change
Snake / Active contour algorithm parameters
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D. Raicu, SCAR 2005

19. HEART: Segmentation (cont.)
Button is clicked
User selects points
around the region of interest
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D. Raicu, SCAR 2005

20. HEART: Segmentation Show segmented organ
If the user likes the result of the segmentation,
then the user will go to the classification step
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D. Raicu, SCAR 2005

21. HEART: Classification Selection of texture models
Texture features corresponding to the selected texture model are calculated and shown here
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D. Raicu, SCAR 2005

22. HEART: Classification
Results are shown as follows:
Predicted organ: Heart
Probability: 0.86
Rule used
to predict that this segmented
organ is HEART
9/19/2018
D. Raicu, SCAR 2005

23. Following Research Projects
Project 1: Find normal tissues in CT images
A. Based on segmented organs
Computer –Aided Diagnosis (CAD) tools
for lung cancer:
Tool 1
Tool 2 …
heart
lung
backbone
Goal: provide context-sensitive tools for abnormality detection & classification
9/19/2018
D. Raicu, SCAR 2005

24. Following Research Projects
Project 1: Find normal tissues in CT images
B. Based on pure patches
Goal: Develop a collection of region-of-interests (ROIs) of various
tissues in normal computed tomography studies.
9/19/2018
D. Raicu, SCAR 2005

25. Following Research Projects
Project 2: Binning strategies for co-occurrence texture models
Linear binning
Clipped binning
Presentation will be given by Roman on linear and clipped binning
C. Non-linear binning
Goal: Reduce the number of gray-levels in an image such that
the amount of information still present in the image will allow to
differentiate among different organs/tissues
9/19/2018
D. Raicu, SCAR 2005

26. References
Haralick, R.M., K.Shanmugam, & I. Dinstein. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, vol. Smc-3, no.6, Nov pp
Xu, C. & J.L. Prince. Gradient Vector Flow: A New External Force for Snakes. IEEE Proceedings of Conference on Computer Vision & Pattern Recognition, 1997.
R. Gonzalez & R. Woods. Digital Image Processing, Prentice Hall, Inc. 2002
9/19/2018
D. Raicu, SCAR 2005