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Tumor Discrimination Using Textures

Tumor Discrimination Using Textures. Presented by: Maysam Heydari. 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). Introduction.

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Tumor Discrimination Using Textures

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  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 Maysam segmented (low grade tumors) Expert segmented # 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 0.3600 0.3600 0.1000 1st MR8 0.2800 0.3800 0.4000 2nd MR8 0.2600 0.3800 0.4200 3rd MR8 0.4000 0.3800 0.2800 4th MR8 0.3000 0.3800 0.4000 5th MR8 0.2400 0.3600 0.4000 6th MR8 0.2800 0.3800 0.3600 7th MR8 0.3400 0.4000 0.1200 8th MR8 0.4000 0.4200 0.1400 Energy 0.4400 0.3200 0.4600 Contrast 0.3800 0.2800 0.4200 BGLAM 0.3200 0.2000 0.4200

  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 0.3200 0.3200 0.1400 0.3600 0.4600 0.4400 0.3400 0.4600 0.4200 0.4800 0.4600 0.2400 0.3800 0.4600 0.4800 0.3200 0.4400 0.4400 0.3600 0.4600 0.3600 0.4200 0.4800 0.2000 0.4800 0.5000 0.2200 0.4400 0.3200 0.4600 0.3800 0.3600 0.3800 0.3200 0.2000 0.4200

  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?

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