1. Wavelet-based texture analysis and segmentation
MedIX – Summer 05
Wavelet-based texture analysis and segmentation

2. Done so far …
Given a pre-segmented organ region, can you tell me what it is: kidney, heart etc?
It depends … on its texture
Texture features extracted from the wavelet transform of the image
Wavelet-based texture classification of organ tissues from CT- scans

3. Wavelet transform (an example)
Averages
Horizontal Activity
Vertical Activity
Diagonal Activity

4. Haar Wavelet 9 7 3 5 8 4 1 -1 6 2 1 -1 Original image 6 2 1 -1D A D
Original image 9 7 3 5 A D A D 8 4 1 -1
Averages
Details 6 2 1 -1 6 2 1 -1
Wavelet coefficients AA AD DA DD

5. Texture Classification Process
Improving the
Wavelet
Coefficients
Texture
Descriptors
Organ/Tissue
Image
Other Wavelets
Wavelet frames
Gabor filters
Other descriptors
Features
reduction
Decision Trees
Classification rules
for tissue/organs in
CT images
Different classifier
Similarity measures
Different criteria for picking the most discriminatory features

6. Texture segmentation
Given an image, can you tell me which/how many organs you have?
Identifying regions with similar texture

7. Wavelet-based texture segmentation
Block-wise segmentation using feature extracted from the wavelet transform
Which distance b/w blocks?
Which features?
What threshold?
Split and merge techniques
Pixel-level texture segmentation

8. On the fence
Ridgelets: a variation of the wavelet transform particularly efficient in detecting line-like edges/phenomena
Curvelets: a variation of wavelets/ridgelets particularly efficient in detecting curve-like edges/phenomena
Application to segmentation