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Tumor Discrimination Using Textures Presented by: Maysam Heydari.

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Presentation on theme: "Tumor Discrimination Using Textures Presented by: Maysam Heydari."— Presentation transcript:

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 weeks # of patients Maysam segmented (low grade tumors)

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 T1T1CT2 raw 3rd MR8 6th MR8 7th MR8 Patient: 145 Study: 2

10 T1T1CT2 raw energy contrast BGLAM simvals Patient: 145 Study: 2

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 T1T1CT2 raw 3rd MR8 6th MR8 7th MR8 Patient: 145 Study: 2

13 T1T1CT2 raw energy contrast BGLAM simvals Patient: 145 Study: 2

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 st MR nd MR rd MR th MR th MR th MR th MR th MR Energy Contrast BGLAM

16 Test Results gbm/rest: mismatch rates T1 T1C T 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?


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