Texture Classification of Normal Tissues in Computed Tomography

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.
3
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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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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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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Segmentation Heart
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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.

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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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Texture Feature Extraction
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Organ/Tissue Classification
IF HGRE lt 0.38 AND CLUSTEND lt 0.048 AND
INVDIFFM gt 0.74 AND LRHGE gt 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
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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 0.0001
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)
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Examples of Decision Tree Rules

• IF (HGRE lt 0.38) (CLUSTEND lt 0.05)
  (INVDIFFM lt 0.74) (SUMMEAN gt 0.56) (RLNU gt
  0.02)
• THEN Prediction Renal', Probability 0.94
• IF (HGRE lt 0.38) (CLUSTEND gt 0.05) (SRHGE lt
  0.19) (ENTROPY lt 0.51) (LRLGE gt 0.16)
• THEN Prediction 'Liver', Probability 1.00
• IF (HGRE lt 0.38) (CLUSTEND gt 0.05) (SRHGE lt
  0.19) (ENTROPY gt 0.51) (GLNU gt 0.02)
• THEN Prediction 'Heart', Probability 0.96

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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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Classification Results
Training Data
ORGAN Sensitivity Sensitivity Specificity Specificity Precision Accuracy
Backbone Backbone 99.7 99.5 99.2 99.2 99.6
Liver Liver 80.0 96.9 83.8 83.8 94.1
Heart Heart 84.6 98.5 90.6 90.6 96.5
Renal Renal 92.7 97.9 89.7 89.7 97.1
Splenic parenchyma Splenic parenchyma 79.5 96.1 73.6 73.6 94.1
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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 92.5
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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.
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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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HEART Segmentation
The application allows users to change Snake /
Active contour algorithm parameters
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HEART Segmentation (cont.)
Button is clicked
User selects points around the region of interest
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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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HEART Classification
Selection of texture models
Texture features corresponding to the selected
texture model are calculated and shown here
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HEART Classification
Results are shown as follows Predicted organ
Heart Probability 0.86 Rule used to predict that
this segmented organ is HEART
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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
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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.
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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
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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. 1973. pp. 610-621.
• 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