MedIX – Summer 06
Lucia Dettori (room 745)
ldettori@cti.depaul.edu
Projects

Texture classification
 What
has been done
 Things I would like to explore next
 Connection to other projects

Evaluations of segmentation algorithms
Done so far …
Given a pre-segmented organ region, can
you tell me what it is: kidney, heart etc?
 It depends … on its texture
 Identify image features that give texture
information
 Find rules that distinguish the texture
features of one organ from another

Texture Classification Process at a glance
Organ/Tissue
Segmented Image
Apply filter
To the image
Classification rules
for tissue/organs in
CT images
Texture
Descriptors
Classifier
(Decision Tree)
Step1 – Segmentation and cropping
The image might need to be
cropped, when using filters
that are sensitive to areas of
high contrast (background)
Active Contour Mapping
(Snakes) – a boundary based
segmentation algorithm
Organs
Backbone
Heart
Liver
Kidney
Spleen
Segmented
140
50
56
55
39
Cropped
363
446
506
411
364
Step2 – Filtering the image
Organ/Tissue
Segmented Image
Apply a filter
to the image
For example:
* Co-occurrence matrices
*Run-length matrices
•Wavelets
•Ridgelets
•Curvelets
Wavelet transform
Averages
Horizontal
Activity
Vertical
Activity
Diagonal
Activity
Haar Wavelet
A
D
Original image
9
Averages
8
7
4
3
1
A
D
5
-1
6
2
1
-1
6
2
1
-1
Wavelet coefficients
Details
A
A
D
D
AA
AD
DA
DD
Step3 – Texture features extraction
Organ/Tissue
Segmented Image
Apply a filter
to an image
Texture
Descriptors
Array of texture descriptors
[T1, T2, T3,
…,
Tn]
For example:
Mean, standard
deviation,
energy, entropy
etc..
Step4 - Classification
Organ/Tissue
Segmented Image
Classification
performance
measures
Apply a filter
to an image
Classification rules
for tissue/organs in
CT images
The process of identifying a
region as part of a class (organ)
based on its texture properties.
Texture
Descriptors
Decision
tree
Predicts the organ from the
values of the texture
descriptors
Training / Testing
Step5 – Evaluating the classifier
Misclassification matrix
Actual Category
Backbone Heart
Predicted Backbone
182
6
Category Heart
3
18
Liver
0
3
Kidney
10
4
Spleen
0
0
Total
195
31
Liver
1
4
30
0
4
39
Kidney
6
0
1
49
0
56
Spleen
0
0
7
1
8
16
Performance Measures
Measure
Sensitivity
Specificity
Definition
True Positives / Total Positives
True Negatives / Total Negatives
Precision
Accuracy
True Positives / (True Positives + False Positives)
(True Positives + True Negatives) / Total Samples
Total
195
25
41
64
12
337
Organ
Backbone
Heart
Kidney
Liver
Spleen
Average
Descriptor
Sensitivity
Specificity
Precision
Accuracy
Wavelet
82.6
96.1
82.6
93.7
Ridgelet
91.5
99.3
96.8
98.0
Curvelet
99.4
98.8
95.3
98.9
Wavelet
59.0
92.1
67.0
85.0
Ridgelet
82.5
97.5
88.5
94.6
Curvelet
89.7
99.0
95.5
97.1
Wavelet
77.7
91.4
69.9
88.6
Ridgelet
95.4
93.3
82.0
93.8
Curvelet
96.0
98.1
93.5
97.6
Wavelet
87.3
94.4
82.6
92.8
Ridgelet
86.9
95.9
84.4
94.0
Curvelet
95.9
98.5
94.3
98.0
Wavelet
65.5
94.3
69.7
89.5
Ridgelet
76.9
97.6
88.0
93.8
Curvelet
91.8
98.9
94.9
97.6
Wavelet
74.4
93.7
74.4
89.9
Ridgelet
86.6
96.7
88.0
94.8
Curvelet
94.6
98.7
94.7
97.9
Things I would like to explore
Organ/Tissue
Segmented Image
Apply a filter
to an image
Different patients
Different organs
Abnormal texture
Gabor filters
Fractal Dimensions
Performance
measures
Classification rules
for tissue/organs in
CT images
Texture
Descriptors
Decision
tree
Different modalities
Connections to other project
Can we use wavelet, ridgelet, curveletbased texture descriptors for content
based image retrieval?
 Can we use these descriptors in the
volumetric segmentation?
 Instead of many 2D images, can we use
the same process for 3D stack of slices?

Projects

Texture classification

Evaluations of segmentation algorithms
 What
has been done
 Things I would like to explore next
 Connection to other projects
Texture segmentation
Given an image, can you tell me
how many organs you have?
 That was easy enough. Can you
tell which organs they are?

 Identifying
regions with similar texture
 Identifying which texture it is to label the
organ
A couple of key questions

Can you do it better by varying a
parameter? How do you choose the
values of your segmentation parameters?

If it looks better is it really better?
A couple of key questions

Parameter optimization

Performance evaluation
1
0.87
3
0.56
4
2
0.50
0.75
Ground
Truth
Regions
key
Machine
Segmentations
Increasing value of a segmentation parameter
How do I decide
what the optimal
value of the
parameter is?
How good a
segmentation is it?
The “goodness” metric

A single value that assigns a rating to a
particular segmentation based on how well
the machine segmented regions “match”
the regions in the ground truth images
Region Categories
Ground Truth vs. Machine Segmented
 Correctly Detected
 Over Segmented
GT
 Under Segmented
 Missed
MS
 Noise
OVER SEGMENTED
CORRECTLY
DETECTED
Index for each
region
UNDER SEGMENTED


A Missed region is a GT region
that does not participate in any
instance of CD, OS, or US
A Noise region is an MS region
that does not participate in any
instance of CD, OS, or US
The “Goodness” Metric
good = Correct Detection Index
 bad = 1-Correct Detection Index
 goodness = good-bad*weight

1.0
Ceiling = CDind
Weight Range = CDind-1
Floor = 2*CDind-1
-1.0
How can we use the metric?




Create a set of ground truth mosaic using
radiologist-labels images of pure patches of
organ tissues
Apply segmentation algorithm
Optimize the segmentation parameters using the
metric
Apply optimized algorithm to the “real” image
Ground Truth
T=1000; GM= - .94
T=2000; GM= - .02
T=4000; GM= .74
T=5000; GM= .75
Region key
T=3000; GM= .73
T=6000; GM= .08
Done so far

Used the metric on a block-wise walevetbased segmentation algorithm on some
sample mosaic
To be done
Fully test the metric on a wide range of
segmentation algorithms
 Decouple the various components of the
metric and test the individual performance
measures instead of the overall score
 Extend the metric to measure one region
vs background segmentation

To be done
Improve the wavelet-based algorithms we
have implement to include other texture
features
 Explore and compare other texture-based
segmentation algorithm
 Use regions and metric to calculate
changes in time of an abnormal region

Connections to other projects

Use one of these algorithms to create a
rough segmentation that will generate the
starting point for a more sophisticated
segmentation algorithm.
Some references





”Wavelet-based Texture Classification of Tissues in Computed
Tomography”, L. Semler, L, Dettori, and Jacob Furst.
18th IEEE International Symposium on Computer-based Medical Systems,
Dublin, Ireland, June 2005.
“Ridgelet-based Texture Classification in Computed Tomography”, L.
Semler, L. Dettori. and W.Kerr. 8th IASTED International Conference on
Signal and Image Processing, Honolulu, HW, August 2006.
“Curvelet-based Texture Classification of Tissues in Computed
Tomography”, L. Semler, & L. Dettori. International Conference on Image
Processing, Atlanta, GA, October 2006.
“A Comparison of Wavelet-based and Ridgelet-based texture classification
of Tissues in Computed Tomography”, with Lindsay Semler, International
Conference on Computer Vision Theory and Applications, Setubal, Portugal,
February 2006
“A Methodology and Metric for Quantitative Analysis and Parameter
Optimization of Unsupervised, Multi-Region Image Segmentation”, William
Kerr, Lucia Dettori, and Lindsay Semler, 8th IASTED International
Conference on Signal and Image Processing, Honolulu, HW, August 2006.