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

1. Multi-scale directional
filtering based
method for Follicular
Lymphoma grading
ALİCAN BOZKURT, A. ENIS CETIN
MUSCLE WORKSHOP, ANTALYA
03.10.2013

2. 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

3. Follicular Lymphoma
grading
Grade 1
Grade 2
Grade 3
3

4. 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

5. 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

6. 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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7. 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. 8

9. Directional filtering
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10. Directional Filtering
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11. Directional filtering
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12. 12

13. Directional Filtering
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14. Feature extraction
Step 0
• Input Image
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15. Feature extraction
• Input Image
Step 0
Step 1
• Convert Image
to gray level
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16. 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

17. 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

18. Dataset
 Same dataset used by Suhre
 90 images per grade
Grade 1
Grade 2
Grade 3
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19. Background
19

20. 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.
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21. 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
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22. Background
22

23. Results
23

24. 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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25. Thank You!
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