Multi Scale Directional Filtering Based Method for Follicular Lymphoma Grading
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Multi Scale Directional Filtering Based Method for Follicular Lymphoma Grading

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from MUSCLE International Workshop on Computational Intelligence for Multimedia Understanding, 3-4 October 2013, Antalya, Turkey

from MUSCLE International Workshop on Computational Intelligence for Multimedia Understanding, 3-4 October 2013, Antalya, Turkey

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Multi Scale Directional Filtering Based Method for Follicular Lymphoma Grading Multi Scale Directional Filtering Based Method for Follicular Lymphoma Grading Presentation Transcript

  • Multi-scale directional filtering based method for Follicular Lymphoma grading ALİCAN BOZKURT, A. ENIS CETIN MUSCLE WORKSHOP, ANTALYA 03.10.2013
  • Follicular Lymphoma grading • Follicular Lymphoma (FL) • Presence of a follicular or nodular pattern of growth presented by follicle center B cells • centrocytes and centroblasts. Grade 1 (0-5) Grade 2 (6-15) Grade 3 (>15) 2
  • Follicular Lymphoma grading Grade 1 Grade 2 Grade 3 3 View slide
  • Follicular Lymphoma grading • Pioneer work by Sertel et al: • mimicked the manual approach of pathologists, i.e., identifying the number of centroblasts in the sample. Based on this, a decision on the grade of the sample can be made. • Accuracy for CB detection was about 80%. Sertel, Olcay, et al. "Histopathological image analysis using model-based intermediate representations and color texture: Follicular lymphoma grading." Journal of Signal Processing Systems 55.1-3 (2009): 169-183. 4 View slide
  • Follicular Lymphoma grading • Improvement by Suhre • Hp and Ep denote the projections on the H and E vectors proposed by Cosatto et al. (2008) to model Hematoxylin and Eosin (H&E) staining. • Grades (1,2) and 3 can be distinguished by comparing the histograms via Kullback-Leibler (KL) divergence. • For differentiating grades 1 and 2, we choose a Bayesian classifier. (DCT of the eigenvalue histograms) The underlying PDF is assumed to be sparse, therefore only q coefficients are used. Grade 1 Grade 2 Grade 3 98.89 98.89 100 5
  • Follicular Lymphoma grading • Our Work • • • • Approaches the problem as texture recognition program Based on a novel multi-scale feature extraction method LDA SVM 6
  • Directional filtering •Main idea: rotating a 1D filter along desired orientation •Easy for θ=k x 45°, k=0,1,2,… •Not easy for θ≠k x 45° • Bilinear/cubic interpolation • Our method: coefficients proportional to length of line segments enclosed by pixels • Also used in CT Herman, Gabor T. "Image reconstruction from projections." Image Reconstruction from Projections: Implementation and Applications 1 (1979). 7
  • 8
  • Directional filtering 9
  • Directional Filtering 10
  • Directional filtering 11
  • 12
  • Directional Filtering 13
  • Feature extraction Step 0 • Input Image 14
  • Feature extraction • Input Image Step 0 Step 1 • Convert Image to gray level 15
  • Feature extraction Step 0 μ1 : σ1 : 0,082091 0,084891 0,060045 0,080689 0,085836 0,060873 0,14791 0,15201 0,11201 0,14617 0,15402 0,11424 50 50 50 50 100 100 100 100 100 150 150 150 150 150 150 200 200 200 200 200 200 250 250 250 250 250 250 300 300 50 100 300 μ2: σ2: 300 50 100 150 200 250 300 350 400 450 500 300 300 50 50 100 150 200 250 300 350 400 450 500 50 100 150 200 250 300 350 400 450 100 150 200 250 300 350 400 450 500 500 50 50 100 150 200 250 300 350 400 450 500 50 100 150 200 250 300 350 400 450 0,22597 0,24064 0,11976 0,23731 0,24072 0,36203 0,35692 0,17401 0,37765 0,34842 500 0,12753 Step 2 • Convert Image to gray level • Extract Features 0,19024 20 20 40 40 60 60 60 60 60 60 40 40 40 40 20 20 20 20 80 80 80 80 100 100 100 100 100 120 120 120 120 80 80 1 00 1 20 140 1 60 50 10 0 150 200 250 140 160 50 100 150 200 250 100 150 2 00 140 160 50 50 120 140 160 140 160 1 40 μ3: σ3: Step 1 • Input Image 100 150 200 250 250 160 50 100 150 200 25 0 50 100 150 200 250 0,49943 0,54883 0,35954 0,55623 0,56736 0,30949 0,6949 0,46078 0,72141 0,68851 0,39779 0,65361 Φ = [μ1 σ1 μ2 σ2 μ3 σ3] (1x36 feature vector) 16
  • Classification [ [ [ θ1 θ2 . . . θN features ] ] PCA Test 10fold CV ] LDA Training SVM Train Par am ete r sea rch for C an dγ Model SVM Classify Mean Accuracy 17
  • Dataset  Same dataset used by Suhre  90 images per grade Grade 1 Grade 2 Grade 3 18
  • Background 19
  • Results Follicular Lymphoma •Max: 100.00 (Dir. Fil.) •SoA: 99.26 [20] A. Suhre, Novel Methods for Microscopic Image Processing, Analysis, Classification and Compression. PhD thesis, Bilkent University, 2013. 20
  • Results 6 Grade 1 Grade 2 Grade 3 4 Feature 2 2 0 -2 -4 -6 -10 -8 -6 -4 -2 Feature 1 0 2 4 6 21
  • Background 22
  • Results 23
  • Conclusion •New directional filter construction and multiscale filtering framework • Computationally efficient (2x faster than the closest competitor) •Follicular Lymphoma Grading as an application of the framework • Mean and standard deviation of directional filter outputs as features • LDA as feature reduction (to 2D) • SVM as classifier • Outperformed state of art 24
  • Thank You! 25