1. Tumor Discrimination Using Textures
Presented by: Maysam Heydari

2. Introduction
Main goal: Discrimination between different tumor grades/types using textural properties
Tumor pathologies:
Grade 2: astro (7), oligo (22)
Grade 3: aa (2), ao (1), amoa (1)
Grade 4: gbm (17)

3. Introduction Patient data: 50 unique patient-study pairs:
25 expert segmented patients
25 Maysam segmented patients
For each patient, the study nearest to the biopsy date (in the range ±52 weeks) was picked.
The nearest biopsy was chosen to determine the pathology.

4. Weeks between study and biopsy
Expert segmented
Maysam segmented
(low grade tumors)
# of patients
weeks
weeks

5. Texture Features
Features extracted on the segmented tumors: ENH (T1, T1C) and EDE (T2) on every slice.
Each pixel in the tumor receives a texture intensity:
Gray Level Co-occurrence Matrices (GLCM)
MR8
BGLAM left-to-right symmetry similarity values

6. Texture Features GLCM stat measures: Energy: “orderliness” of pixels
Contrast:

7. Texture Features MR8 filter bank: For each filter, max response over 6
orientations
Filters:
3 scales of edge filters
3 scales of bar filters
A Gaussian
Laplacian of Gaussian

8. Texture Features BGLAM:
Texture similarity of the segmented tumor to the symmetric side of the brain.

9. Patient: 145
Study: 2 T1
T1C T2
raw
3rd MR8
6th MR8
7th MR8

10. Patient: 145
Study: 2 T1
T1C T2
raw
energy
contrast
BGLAM
simvals

11. Method
For each patient, T1, T1C, and T2 histograms constructed over all the tumor pixels (texture intensities) over all slices.
Histograms normalized and ranges adjusted over all tumors.

12. Patient: 145
Study: 2 T1
T1C T2
raw
3rd MR8
6th MR8
7th MR8

13. Patient: 145
Study: 2 T1
T1C T2
raw
energy
contrast
BGLAM
simvals

14. Method
Each patient’s tumor is represented by a histogram for each modality and texture feature.
The histograms are used as vector inputs to kmeans (k = 2) clustering.

15. Test Results lowgrade/highgrade: mismatch rates T1 T1C T2
Raw
1st MR
2nd MR
3rd MR
4th MR
5th MR
6th MR
7th MR
8th MR
Energy
Contrast
BGLAM

16. Test Results gbm/rest: mismatch rates T1 T1C T2 Raw 1st MR8 2nd MR8
3rd MR8
4th MR8
5th MR8
6th MR8
7th MR8
8th MR8
Energy
Contrast
BGLAM

17. What’s Next? Combine the histograms from several texture features …
Stack them as vectors?
Curse of dimensionality … with only 50 data inputs.
Instead of histograms, use stats: mean, var, min/max?
Supervised learning
SVM?