Multi Scale Directional Filtering Based Method for Follicular Lymphoma Grading
method for Follicular
ALİCAN BOZKURT, A. ENIS CETIN
MUSCLE WORKSHOP, ANTALYA
• Follicular Lymphoma (FL)
Presence of a follicular or
nodular pattern of growth
presented by follicle center B
• centrocytes and
Grade 1 (0-5)
Grade 2 (6-15)
Grade 3 (>15)
• 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.
• 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)
• 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.
• Our Work
Approaches the problem as texture recognition program
Based on a novel multi-scale feature extraction method
•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
• Also used in CT
Herman, Gabor T. "Image reconstruction from projections." Image Reconstruction from Projections: Implementation and Applications 1 (1979).
•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