Multi Scale Directional Filtering Based Method for Follicular Lymphoma Grading

Multi-scale directional filtering based method for Follicular Lymphoma gradingALİCAN BOZKURT, A. ENIS CETIN

MUSCLE WORKSHOP, ANTALYA

03.10.2013

Follicular Lymphoma grading

Grade 1 (0-5)

Grade 2 (6-15)

Grade 3 (>15)

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• Follicular Lymphoma (FL) • Presence of a follicular or

nodular pattern of growth presented by follicle center B cells

• centrocytes and centroblasts.

Follicular Lymphoma grading

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Grade 3Grade 2Grade 1

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%.

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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.

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.

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Grade 1 Grade 2 Grade 3

98.89 98.89 100

Follicular Lymphoma grading

• Our Work• Approaches the problem as texture recognition program• Based on a novel multi-scale feature extraction method• LDA• SVM

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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

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Herman, Gabor T. "Image reconstruction from projections." Image Reconstruction from Projections: Implementation and Applications 1 (1979).

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Directional filtering

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Directional Filtering

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Directional filtering

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Directional Filtering

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Feature extractionStep 0

• Input Image

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Feature extractionStep 0

• Input Image

Step 1

• Convert Image to gray level

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Feature extraction

: 0,082091 0,084891 0,060045 0,080689 0,085836 0,060873

: 0,14791 0,15201 0,11201 0,14617 0,15402 0,11424

: 0,22597 0,24064 0,11976 0,23731 0,24072 0,12753

: 0,36203 0,35692 0,17401 0,37765 0,34842 0,19024

: 0,49943 0,54883 0,35954 0,55623 0,56736 0,30949

: 0,6949 0,65361 0,46078 0,72141 0,68851 0,39779

Φ = [μ1 σ1 μ2 σ2 μ3 σ3]

μ1σ1

μ2σ2

σ3μ3

(1x36 feature vector)

Step 0• Input Image

Step 1

• Convert Image to gray level

Step 2

• Extract Features

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Classification

[ θ1 ][ θ2 ]

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.[ θN ]

10-fold CV

SVMTrain

Paramete

r search for C

and γ

SVMClassifyTraining

Test

Model

features

Mean Accuracy

PCA

LDA

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Dataset Same dataset used by Suhre

90 images per grade

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Grade 3Grade 2Grade 1

Background

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Results

Follicular Lymphoma•Max: 100.00 (Dir. Fil.)•SoA: 99.26

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[20] A. Suhre, Novel Methods for Microscopic Image Processing, Analysis, Classification and Compression. PhD thesis, Bilkent University, 2013.

Results

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Fea

ture

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Results

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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

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Thank You!

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