research: automatic diabetic retinopathy detection

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