This document summarizes a master's thesis on using grey-level co-occurrence matrices (GLCMs) to classify salt textures in seismic images. The thesis tested different GLCM parameters, developed a new "distance GLCM" feature, and evaluated Gaussian classifiers on various feature combinations. Key results included finding isotropic orientation with a 51x51 window size produced optimal GLCMs, and a classifier using contrast, distance GLCM, and weighted energy features performed best visually on test images. The thesis demonstrated GLCMs for salt texture classification but noted improvements could include 3D GLCMs, combining other texture methods, and generalizing the distance GLCM feature beyond specific class mappings.

1. Grey-Level Co-occurrence
features for salt texture
classification
Igor Orlov
Master’s thesis presentation
University of Oslo, Department of Informatics
Supervisor: Anne H Schistad Solberg

2. Content
• Introduction
• Problem statement and motivation
• Background
• Theoretical review of methods used in thesis
• Implementation
• Input data set, finding optimal GLCM parameters
• Evaluation
• Finding the best classifier, testing on training and test images
• Summary

3. Motivation
• Salt structures are closely related to carbohydrates discovery
• Segmentation of salt textures on seismic images is needed to build a
correct velocity model
• Time-consuming process, needs automation

4. Problem statement
Need to segment salt textures on seismic images using grey-level co-
occurrence matrices (GLCMs)
Two main goals:
• Provide an algorithm and visualization tool for finding the optimal
GLCM parameters, such as offset, direction, window size
• Check whether distance matrix between centers of two classes can be
used as a GLCM feature, if yes – estimate performance

5. Seismic data acquisition
• Initial velocity model is based on
mapping of textures to known
classes
• Then updated iteratively
• In areas of salt it is hard to build
a velocity model, mapping is
done manually or semi-aided on
each step

6. Cuts of marine seismic images
Inline slice (vertical cut) Timeslice (horizontal cut)
Data is stored in a 3D cube, can be cut in 2 directions:

7. Salt texture mapping done manually

8. Textures in seismic images - inline
• Sub-horizontal reflectors (yellow)
• Up-dipping and down-dipping
reflectors (orange)
• Salt structures (blue)

9. Textures in seismic images - timeslice
• Sub-horizontal reflectors
(yellow)
• Steep-dipping reflectors (orange)
• Salt structures (blue)
Not considered in this thesis

10. Background for research methods
• Grey-level co-occurrence matrices – GLCM
• GLCM features
• Distance metrics
• Gaussian classifier

11. Grey-level co-occurrence matrix
• Texture description method –
intensity change histogram
• Matrix where element (x,y)
represents a probability of changing
a pixel intensity from x to y in a local
window of size W when shifting in
direction θ and offset d
• Second order statistic
• Used 5 directions – 0°,45°,90°,135°
and isotropic

12. GLCM features
• Contrast – intensity contrast in a local window, 0 for const
• Correlation
• Energy – sum squared of GLCM elements
• Homogeneity – closeness of distribution in diagonal
• Entropy, Inertia, Cluster Shade etc.

13. Distance metrics
• Used to find a distance between class centers
• Euclidean distance
• Mahalanobis – takes into account in-class distribution (Σ – average
covariance matrix), scale invariant

14. Multivariate Gaussian classifier
• Based on Bayes’ rule
• Assumes Gaussian distribution
• Very simple, trains fast, good
enough
• The likelihood that vector x
belongs to class wj

15. Part 2. Implementation and evaluation
• Input dataset from North Sea
• Finding optimal GLCM parameters, algorithm and visualization
• Mahalanobis distance matrix
• Training Gaussian classifier
• Estimating classification results on training and test images

16. Input image and mask
Original image
Mask. Class 1 – sedimentary rocks (sub-
horizontal), class 2 – salt

17. Masked and normalized

18. Image pre-processing
• Input image 1401x1501, [-346;399]
• Cut histogram tails, normalize and
shift to [0;255]
• No histogram equalization

19. Finding the optimal parameters - algorithm
• Choose direction, offset, window size
• For each pixel of class 1, class 2 calculate the GLCM matrix
• Matrix 16x16 can be presented as a feature vector 1x256
• Calculate the average GLCMs for two classes – class centers
• Build a distance matrix where each element is a pixel-wise Mahalanobis
distance between corresponding elements of class center matrices
• Do the same for other direction, offset and window size, compare norm

20. Mahalanobis distance calculation
• Optimal parameters is where the norm of Mahalanobis distance
matrix between centers of two classes is the greatest

21. GLCM parameters
• 16 grey levels, fixed. Thus, all GLCMs had size 16x16
• Window size 31x31, 51x51
• 5 directions
• Which direction is expected to be best?
• Offset 1..15 for 31x31, 1..20 for 51x51

22. Visualization of distance matrices for 51x51
Horizontal Vertical
• Displaying
Mahalanobis distance
between average
GLCMs for two classes
– salt and
sedimentary rocks

23. Visualization of distance matrices for 51x51
Diagonal Isotropic

24. Norm of distance matrix, 31x31

25. Norm of distance matrix, 51x51

26. Distance matrix for optimal parameters
• Suppress values around the center, enhance values in the middle
and on 3-pixel distance around the center

27. Own GLCM feature
• Distance GLCM also shows which GLCM elements are more important
for separation of two classes
• Pixel-wise multiplication of GLCM for pixel with Mahalanobis distance
matrix, then sum elements
• Can also be used as a weighting window, to strengthen the GLCM
elements which are more important for separation
• Gives us a new set of features – weighted contrast (contrast of GLCM
weighted with distMatrix), weighted energy etc.

28. Feature list
• For each pixel of class 1 and class 2 we have calculated the following
ten features:
• Variance
• GLCM contrast + weighted
• GLCM energy + weighted
• GLCM correlation + weighted
• GLCM homogeneity + weighted
• Own GLCM feature – sum of weighted GLCM elements

29. Gaussian classification
• Trained classifier on one-, two-, three-feature subsets – taken
manually in a random way
• Some where correlated, overtraining
• One feature is not enough
• Tested on original image (can estimate absolute performance) and 2
test images – only visual estimation

30. Single feature - Contrast

31. Single feature – distance GLCM (own)

32. Single feature – Energy (Weighted)

33. Classification results for single feature
• For training image, class
mapping was provided
• TN – true negative (correctly
classified rocks), FP – false
positive (rocks classified as salt),
TP – true positive (correctly
classified salt).

34. Contrast and Correlation
• Good TN and TP rate is not a
guarantee of good results
• TN – 94,17%, TP – 91,8%

35. Energy and Variance

36. Energy, Own feature and Variance

37. Contrast, Energy and Own

38. Contrast, Own and Weighted Energy

39. Classification results for multiple features

40. Test images from same dataset
inlinekorr700 inlinekorr780

41. Classification on test image inlinekorr700

42. Classification on test image inlinekorr780

43. Summary - Achieved results
• Found the optimal GLCM parameters for separation of 2 classes:
• Isotropic, offset 2, window 51x51
• Invented and tested own GLCM feature:
• Mahalanobis distance between average GLCMs for two classes
• Trained and tested a set of Gaussian classifiers to find the best feature
combination for salt texture extraction
• Visually best is Contrast, Own GLCM and Weighted Energy

44. Summary – Disadvantages and drawbacks
• Only dataset from North Sea was investigated
• Maximum dimension of Gaussian classifiers was 3
• Only variance, standard Matlab GLCM features (4) and own GLCM feature were
tested
• Feature selection done manually
• Only visual testing for test images

45. Summary - Possible improvements
• 3D GLCM – spatial relationship
• Combination with other texture analysis methods – Fourier transform, greater
order statistics etc.
• Own feature – Mahalanobis distance - is based on pixel class mapping. Needs
generalization (formula)

46. The end
Thanks for attention, questions…