research: automatic diabetic retinopathy detection
TRANSCRIPT
M.K.H. GUNASEKARACSC 363 1.5 Research Methodologies and Scientific ComputingDepartment of Computer Science and Statistics , USJPAS2010377
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Automatic detection of diabetic retinopathy hard exudates using
mathematical morphology methods and fuzzy logic
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Future WorksRelated Works
IntroductionLiterature Review
ResultsImplementation
Methodology
Overview
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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.
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Introduction
Aim of this research is to develop system for detection of hard exudates in diabetic retinopathy using non-
dilated diabetic retinopathy images
Abnormalities of Diabetic Retinopathy• Microaneurysms• Hemorphages• Cotton wool spots ( Soft Exudates)• Hard Exudates
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Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman and Thomas H. Williamson.Automatic detection of diabetic retinopathy exudates from non-dialed retinal images using mathematical morphology methods.Computerized Medical Imaging & Graphic
Akara Sopharak and Sarah BarmanAutomatic Exudate Detection from Non-dilated Diabetic Retinopathy Images Using Fuzzy C- means Clustering.Journal of Sensors.
Literature Review
V.Vijaya Kumari and N.Suriya Narayanan.Diabetic Retinopathy-Early Detection using Image Processing Techniques.International Journal on Computer Science and Engineering
Berrichi Fatima Zohra, Benyettou Mohamed. Automatic diagnosis of retinal images using the Support Vector Machine (SVM).
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Phase 2Phase 1
Mathematical Morphology
•Exudates are identified using mathematical morphology
Fuzzy Logic
• Identified exudates are classified as hard exudates using fuzzy logic
Methodology
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One• Preprocessing
Two• Optic disc elimination
Three• Exudates detection
Phase 1
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Step 1 Step 2 Step 3 Step 4Input
Fundus Image Color Space Conversion
Median Filtering
Contrast Enhancement
Gaussian Filtering
• Fundus Image is performed by fundus camera
• 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
Preprocessing
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Step 1 Step 2 Step 3 Step 4Input
Preprocessed Image Closing Thresholding
Large Connected component
Optic disc elimination
• Output of preprocessing stage
• Closing operator with flat disc shape structuring element is applied
• Image is binarized
• P-tile method and nilblack’s method
• Connect all regions
Optic Disc Elimination
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Exudates Detection
Input
• Optic disc eliminated
Image
• Standard Deviation
• Remove optic disc boundary • Marker Image • Difference
Image
• Closing • Thresholding • Fill holes • Morphological Reconstruction
• Result is superimposed
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Phase 2
Classification of Hard Exudates using Fuzzy logic
RED
GREEN
BLUE
Outputs
Inputs
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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
𝑓 (𝑥 ,𝜎 ,𝑐 )=𝑒−(𝑥−𝑐)2
2𝜎2
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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
𝑓 (𝑥 ,𝜎 ,𝑐 )=𝑒−(𝑥−𝑐)2
2𝜎2
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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
𝑓 (𝑥 ,𝜎 ,𝑐 )=𝑒−(𝑥−𝑐)2
2𝜎2
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Membership function of Xout
Membership function name
Parameters[sig1 c1 sig2 c2]
NotHardExudate [0.0008493 0 0.06795 0.07]weakHardExudate [0.03 0.35 0.03 0.55]mediumHardExudate
[0.03 0.65 0.03 0.75]
hardExudate [0.03 0.85 0.03 0.9]severeHardExudate
[0.0161 0.9733 0.0256 1]
Gaussian combination membership function
𝑓 (𝑥 ,𝜎 ,𝑐 )=𝑒−(𝑥−𝑐)2
2𝜎2
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Fuzzy rules1 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)
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Implementation
Tested using MATLAB 7.10
• 38 images were used to testing• Images were taken from Kuopio university
hospital • The images’ size were 1500 , 1152 pixels
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Results - Preprocessing
(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
(a)
(f)(e)(d)
(c)(b)
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Results – Optic Disc Elimination
(a)
(f)(e)(d)
(c)(b)
(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
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Results – Exudates Detection
(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 image , (h)- Thresholded image , (i)- Result is super imposed on original image
(a) (c)(b)
(d) (f)(e)
(g) (i)(h)
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Results – Classification of Exudates
(a)- Not exist diabetic retinopathy, (b)- 42% of diabetic retinopathy hard exudates , (c)- 89% of diabetic retinopathy hard exudates ,
(a) (c)(b)
Performance• Overall sensitivity-81.76%• Specificity – 99.96%• Precision – 81%• Accuracy – 99.84%
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Future Works
Tested using MATLAB 7.10
• 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
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Related Work – After Submission
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Related Work – After Submission
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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
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Questions
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Thank You