Automatic Detection of Diabetic Retinopathy Hard Exudates using Mathematical Morphology and Fuzzy logic

1. M.K.H. GUNASEKARA
AS2010377
CSC 363 1.5 Research Methodologies and Scientific
Computing
Department of Computer Science and Statistics , USJP

2. Automatic detection of diabetic
retinopathy hard exudates using
mathematical morphology methods
and fuzzy logic
2

3. Overview
3

4. Introduction
Figure 2: Diabetic macula edema
(swelling of the retina)
Diabetic retinopathy occurs when elevated blood sugar
levels cause blood vessels in the eye to swell and leak
into the retina.
4

5. Introduction
Abnormalities of Diabetic Retinopathy
•
•
•
•
Microaneurysms
Hemorphages
Cotton wool spots ( Soft Exudates)
Hard Exudates
Aim of this research is to develop system for detection
of hard exudates in diabetic retinopathy using nondilated diabetic retinopathy images
5

6. Literature Review
6

7. Methodology
Phase 1
Phase 2
Mathematical Morphology
Fuzzy Logic
• Exudates are identified
using mathematical
morphology
• Identified exudates are
classified as hard exudates
using fuzzy logic
7

8. Phase 1
• Preprocessing
One
• Optic disc elimination
Two
• Exudates detection
Three
8

9. Preprocessing
Input
Fundus Image
• Fundus Image is
performed by
fundus camera
Step 1
Step 2
Step 3
Step 4
Color Space
Conversion
Median
Filtering
Contrast
Enhancement
Gaussian
Filtering
• RGB color space
in the image in
converted to HIS
space
• Noise
suppression
• Contrast limited
adaptive
histogram
equalization was
applied for
contrast
enhancement
• Noise
Suppression
further
9

10. Optic Disc Elimination
Input
Preprocessed
Image
• Output of
preprocessing
stage
Step 1
Closing
• Closing operator
with flat disc
shape
structuring
element is
applied
Step 2
Step 3
Step 4
Thresholding
Large
Connected
component
Optic disc
elimination
• Image is
binarized
• P-tile method
and nilblack’s
method
• Connect all
regions
10

11. Exudates Detection
• Optic disc
eliminated
Image
• Standard
Deviation
• Remove optic
disc boundary
• Marker Image
• Difference
Image
I
n
p
u
t
• Closing
• Thresholding
• Fill holes
• Morphological
Reconstruction
• Result is
superimposed
11

12. Phase 2
RED
Outputs
Inputs
GREEN
BLUE
Classification of Hard Exudates using Fuzzy logic
12

13. Membership function of XR
Membership
function name
Parameters
[sig1 c1 sig2 c2]
R1
[0.016 0 8.617 57.85]
R2
[3 78 3 87]
R3
[3 100 3 111]
R4
[3 125 3 144]
R5
[3 156 3 168]
R6
[3 180 3 193]
R7
[3 205 0.2166 255]
Gaussian combination membership function
13

14. Membership function of XG
Membership
function name
Parameters
[sig1 c1 sig2 c2]
G1
[0.217 0.8 8.14 31.55]
G2
[3 54 3 65]
G3
[3 76 3 86]
G4
[3 98 3 108]
G5
[3 120 3 134]
G6
[3 146 3 220]
G7
[3 232 3 255]
Gaussian combination membership function
14

15. Membership function of XB
Membership
function name
Parameters
[sig1 c1 sig2 c2]
B1
[0.217 0 3.081 5.408]
B2
[3 17 3 50]
B3
[3 60 3 102]
B4
[3 112 3 255]
Gaussian combination membership function
15

16. Membership function of Xout
Membership
function name
NotHardExudate
Parameters
[sig1 c1 sig2 c2]
[0.0008493 0 0.06795 0.07]
weakHardExudate [0.03 0.35 0.03 0.55]
mediumHardExud
ate
[0.03 0.65 0.03 0.75]
hardExudate
[0.03 0.85 0.03 0.9]
severeHardExudat [0.0161 0.9733 0.0256 1]
e
Gaussian combination membership function
16

17. Fuzzy rules
1
If (Xr is R1) Or (Xg is G1) Or (Xb is B4) Then (Xout is notHardExudate)
2
If (Xr is R2) And (Xg is G2) Or (Xb is B1) Then (Xout is weakHardExudate)
3
If (Xr is R2) And (Xg is Not G2) And (Xb is Not B1) Then (Xout is notHardExudate)
4
If (Xr is R3) And (Xg is G3) And ((Xb is B1) Or (Xb is B2) ) Then (Xout is weakHardExudate)
5
If (Xr is R3) And (Xg is G3) And (Xb is B3) Then (Xout is notHardExudate)
6
If (Xr is R3) And (Xg is Not G3) Then (Xout is notHardExudate)
7
If (Xr is R4) And (Xg is G3) And (Xb is B1) Then (Xout is mediumHardExudate)
8
If (Xr is R4) And (Xg is G3) And (Xb is B2) Then (Xout is weakHardExudate)
9
If (Xr is R4) And (Xg is Not G3) Then (Xout is notHardExudate)
10
If (Xr is R5) And ((Xg is G2) Or (Xg is G3) Or (Xg is G4)) Then (Xout is notHardExudate)
11
If (Xr is R5) And (Xg is G5) And ((Xb is B1) Or (Xb is B2)) Then (Xout is HardExudate)
12
If (Xr is R5) And ((Xg is G6) Or (Xg is G7)) Then (Xout is notHardExudate)
13
If (Xr is R5) And (Xb is B3) Then (Xout is notHardExudate)
14
If (Xr is R6) And ((Xg is G2) Or (Xg is G3)) Then (Xout is notHardExudate)
15
If (Xr is R6) And (Xg is G4) And ((Xb is B1) Or (Xb is B2)) Then (Xout is HardExudate)
16
If (Xr is R6) And (Xg is G5) And ((Xb is B1) Or (Xb is B2)) Then (Xout is HardExudate)
17
If (Xr is R6) And (Xg is G6) And ((Xb is B1) Or (Xb is B2)) Then (Xout is HardExudate)
18
If (Xr is R6) And (Xg is G7) Then (Xout is notHardExudate)
19
If (Xr is R6) And (Xb is B3) Then (Xout is notHardExudate)
20
If (Xr is R7) And (Xg is G6) And ((Xb is B1) Or (Xb is B2) Or (Xb is B3)) Then (Xout is severeHardExudate)
21
If (Xr is R7) And (Xg is G5) And ((Xb is B1) Or (Xb is B2)) Then (Xout is notHardExudate)
22
If (Xr is R7) And ((Xg is G2) Or (Xg is G3) Or (Xg is G4)) Then (Xout is notHardExudate)
17

18. Implementation
• 38 images were used to testing
• Images were taken from Kuopio university
hospital
• The images’ size were 1500 , 1152 pixels
Tested using MATLAB 7.10
18

19. Results - Preprocessing
(a)
(b)
(c)
(d)
(e)
(f)
(a)-Original Fundus Image , (b)-HSI Image, (c)– Intensity band of
Image, (d)- Median Filtering, (e)- Applying Contrast limited
Adaptive histogram equalization, (f)- Gaussian Filtering
19

20. Results – Optic Disc Elimination
(a)
(b)
(c)
(d)
(e)
(f)
(a)-Applying morphological closing operator, (b)-Thresholded image using
Nilblack’s method, (c)– Thresholded Image using percentile method,
(d)- Large circular connected component, (e)-Inverted binary image,
(f)- Optic disc is eliminated from the preprocessed image
20

21. Results – Exudates Detection
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(a)- Applying morphological closing operator , (b)- Standard deviation of the image ,
(c)-Thresholded image using triangle method , (d)- Unwanted borders were removed ,
(e)- Holes are flood filled , (f)- Marker Image , (g)- Morphological reconstructed image21
,
(h)- Thresholded image , (i)- Result is super imposed on original image

22. Results – Classification of Exudates
(a)
(b)
(c)
Performance
•
•
•
•
Overall sensitivity-81.76%
Specificity – 99.96%
Precision – 81%
Accuracy – 99.84%
(a)- Not exist diabetic retinopathy,
(b)- 42% of diabetic retinopathy hard exudates ,
(c)- 89% of diabetic retinopathy hard exudates ,
22

23. Future Works
•
•
•
•
Preprocessing Stage
Optic Disc Elimination
Exudates Detection
Classification of Exudates as Hard
Exudates
• Exudative Maculopathy Detection
• Support Vector Machines, K Means
Algorithms, Radial Basis Functions
Tested using MATLAB 7.10
23

24. Related Work – After Submission
24

25. Related Work – After Submission
25

26. References
• Meysam Tavakoli, Reza Pourreza Shahri, Hamidreza Pourreza, Alireza Mehdizadeh,
Touka Banaee, Mohammad Hosein Bahreini Toosi, A complementary method for
automated detection of microaneurysms in fluorescein angiography fundus images
to assess diabetic retinopathy, Pattern Recognition, Volume 46, Issue 10, October
2013, Pages 2740-2753, ISSN 0031-3203,
http://dx.doi.org/10.1016/j.patcog.2013.03.011.
(http://www.sciencedirect.com/science/article/pii/S0031320313001404)
• M. Usman Akram, Shehzad Khalid, Shoab A. Khan, Identification and classification
of microaneurysms for early detection of diabetic retinopathy, Pattern Recognition,
Volume 46, Issue 1, January 2013, Pages 107-116, ISSN 0031-3203,
http://dx.doi.org/10.1016/j.patcog.2012.07.002.
(http://www.sciencedirect.com/science/article/pii/S003132031200297X)
• R.H.N.G. Ranamuka, Automatic detection of diabetic retinopathy hard exudates
using mathematical morphology methods and fuzzy logic, Graduation Thesis,
University of Sri Jayewardenepura, 2011
26

27. Questions
27

28. Thank You
28