Research: Automatic Diabetic Retinopathy Detection
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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

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

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  • Microaneursms is the early stage of Diabetic Retinopathy
  • Akara’s suggested certain steps for optic disc detection prior to the exudates identification.After the optic disc elimination mathematical morphology has been used for the exudates detectionAkara proposed a Fuzzy C Means (FCM) clustering method for exudates detectionSuggested a computer based approach for automated classification of Normal, NPDR and DPRThey have used the green layer for exudates detection because they have discovered that the brightness area including exudates of retinal image is in green layer in literature
  • Intensity band of the HIS image is used at this stageFirstly RGB color space in the original fundus image is converted to HIS (HUE, Intensity and saturation) space.Then median filter is applied for the intensity band of the image for the noise suppression. Median filter is non linear median filter which is used to remove noises in an image with minimal degradation to edges.Subsequently the Contrast limited adaptive histogram equalization was applied for contrast enhancement this adaptive histogram method is used to improve the local contrast of an image. It may be produce a significant noiseGaussian function is applied for noise suppression furtherThis gaussian filtering function is used to filter out the noise in the image without compromising on the region of interest
  • Firstly the closing operator with a flat disk shape structuring element is applied for the preprocessed image.Then the result image is binarized using thresholdingtechniqueClosing is a morphological operation
  • Remove optic disc boundaryTriangle method is used to obtain thesholded imageFill HolesMarker image The intensity band of original image is selected as the mask image.Morphological ReconstructionDifference Image
  • I have used the RGB color space values of retinal image to form the fuzzy set and the membership functionsNot Hard ExudatesWeak Hard ExudatesMedium Hard ExudatesHard ExudatesSevere Hard Exduates
  • There are 7 linguistic variables for Xr
  • There are 7 linguistic variables for Xg
  • There are 4 linguistic variables for Xr
  • There are 5 linguistic variables for output value
  • Fuzzy rules
  • Those 38 images were publicly available diabetic retinopathy dataset
  • Those 38 images were publicly available diabetic retinopathy dataset
  • Proposed Radon Transform method to detect MAs
  • ProposedHybrid Classifier which combines Gaussian Mixture Model and Support Vector Machine
  • Those 38 images were publicly available diabetic retinopathy dataset

Transcript

  • 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