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Research: Automatic Diabetic Retinopathy Detection

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

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Research: Automatic Diabetic Retinopathy Detection

  1. 1. M.K.H. GUNASEKARA AS2010377 CSC 363 1.5 Research Methodologies and Scientific Computing Department of Computer Science and Statistics , USJP
  2. 2. Automatic detection of diabetic retinopathy hard exudates using mathematical morphology methods and fuzzy logic 2
  3. 3. Overview 3
  4. 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. 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. 6. Literature Review 6
  7. 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. 8. Phase 1 • Preprocessing One • Optic disc elimination Two • Exudates detection Three 8
  9. 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. 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. 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. 12. Phase 2 RED Outputs Inputs GREEN BLUE Classification of Hard Exudates using Fuzzy logic 12
  13. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 24. Related Work – After Submission 24
  25. 25. Related Work – After Submission 25
  26. 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. 27. Questions 27
  28. 28. Thank You 28

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