1. A New Classification Mechanism for Retinal Images
Chair Professor Chin-Chen Chang
Feng Chia University
National Chung Cheng University
National Tsing Hua University

2. Outline Introduction Related works - Line detector
- Principal component analysis (PCA)
- Support vector machine (SVM)
Proposed scheme
- SVM
- PCA + SVM
Experimental results
Conclusions

3. The classification mechanism for retinal images
Introduction (1/3)
The retinal images
The non-retinal images
Classification
The classification mechanism for retinal images
The medical image set

4. Introduction (2/3) Specific direction of blood vessels
The large differences between blood vessels and their background

5. Introduction (3/3) (a) Color retinal image (b) Red- plane of (a)
(c) Green- plane of (a)
(d) Blue- plane of (a)

6. Line detector (1/2) The pre-process of line detector
(a) The color retinal image
(b) Green-plane of (a)
(c) Inverted green-plane of (a)

7. Line detector (2/2) Line detector Mean value of pth line is Lp
15° j 0°
Setting a threshold i
Mean value of pth line is Lp
L(i, j)=MAX(L1, L2, …, L12)
The average of this window is N

8. Principal component analysis (1/6)
Projection value X
PC1 Y
An image

9. Principal component analysis (2/6)

10. Principal component analysis (3/6)
Sorting
Sorting

11. Principal component analysis (4/6)
Projection value
PRi = Pk(Ii-)

12. Principal component analysis (5/6)

13. Principal component analysis (6/6)
d =
d =
d =

14. Support vector machine (1/2)
Training
The color retinal image
Trained
model
(20 images)
SVM
The non-retinal image
(20 images)
The training image set

15. Support vector machine (2/2)
Optimal hyperplane
Class 1
Hyperplane
Support
vector
Class 2

16. Proposed scheme (1/4) Two classification mechanisms: Scheme-1 Scheme-2
Support vector machine (SVM)
Scheme-2
Principal component analysis (PCA) + Support vector machine (SVM)

17. Proposed scheme (2/4) Scheme-1: Support vector machine (SVM) Trained
The test image set
The green-plane of the color retinal image (20 images)
(20 images)
(20 images)
SVM
Trained
model
The segmentation with line detector
Accuracy rate
The training image set
The non-retinal image (20 images)

18. Proposed scheme (3/4) Scheme-2: PCA + SVM
The feature values of the retinal images
The green-plane of the color retinal image (20 images)
PCA
The feature values of the non-retinal images
The segmentation with line detector
The non-retinal image (20 images)
The training image set

19. Proposed scheme (4/4) Scheme-2: PCA + SVM Trained SVM model
The test image set
PCA
The feature values of the retinal images
(20 images)
(20 images)
SVM
Trained
model
The feature values of the non-retinal images
Accuracy rate
The training image set

20. Experimental results
Comparison of PCA+SVM and SVM accuracy rates

21. Conclusions The method is very simple.
The method can achieve high performances for classification of the retinal images.