The document discusses the automatic detection of diabetic maculopathy from fundus images using image analysis techniques, highlighting the importance of identifying various retinal features. It details the methods for detecting both normal and abnormal features in retinal images, including the optic disk, macula, and exudates, as well as the severity classification of diabetic maculopathy. The proposed system demonstrates high accuracy rates in processing and analyzing retinal images, achieving significant performance in detecting various features related to diabetic retinopathy.

1. Automatic Detection of Diabetic
Maculopathy from Fundus Images Using
Image Analysis Techniques.
Submitted By:-
Eman Abdulalazeez Gani Aldhaher
1436-2014

2. The Human Eye
 Eye is an organ associated with vision.
 The abnormalities associated with the eye can be divided in
two main classes:-
Eye diseases such as:- cataracts, glaucoma.
 Life style related disease such as:- hypertension, diabetes.

3. Diabetes Mellitus
 Diabetes is a chronic metabolic disorder caused by
either the pancreas either produced too little or no
insulin or the cells do not react properly to the
insulin that is produced.
 Diabetes can harm eye by damaging blood vessels of
eye retina, which in turn can cause loss of vision.
 According to World Health Organization (WHO),
number of adults with diabetes in the world would
increase alarmingly from 135 million in 1995 to 483
million in 2030.

4. The Human Retina
 The retina, also called fundus image, is a multi-layered sensory
tissue that lies on the back of the eye. It capture light rays and
convert them into electrical impulses that travel along the optic
nerve to the brain where they are turned into image.

5. Physiology of Retina
 Optic disk
 Macula
 Fovea
 Vascular Network
Optic Disk Macula
Fovea
Vascular Network

6. Abnormal Lesion of
Diabetic Retinopathy
 Exudates
 Hard Exudates
 Soft Exudates

7. Diabetic Maculopathy
 Diabetic Maculopathy occurs if exudates appear near the
macula affecting central vision stage.
Normal eye Eye with Diabetic Maculopathy

8. Diabetic Maculopathy
 The severity level of Diabetic Maculopathy is classified in to:-
 Sever
 Moderate
 Mild 1/3DD
1DD
2DD

9. Color Bands Analysis
 The input images has orange dominate color which indicates
that the blue channel doesn't have significant information.

10. Fundus Region Detection
 Improve the efficiency of the system by extracting background
and removing the damaged areas from retinal image to
allocate the actual region of interest (ROI).
Background Area
Background Area
Damaged Areas
Damaged Areas

11. Background Elimination
 A color retinal image consist of a semi circular fundus and
dark background surrounding it which is not clear homogenous
black area. The background area is omitted using :-
Thresholding, Dilation, Mask Generation.

12. Damaged Areas
Segmentation
 Damaged or non-informatics areas in color retinal image
is usually due to pixels whose color is distorted; they exist
in some parts of the fundus boundary regions where
illumination was not inadequate.
 Caused by a number of factors, including retinal
pigmentation, acquisition angle, inadequate illumination,
cameras' differences and patient movement.
 Poor image quality region are detected using three steps:-
 Max-Min Detector.
 Ratio Detector
 Seed Filling Algorithm

13. Max-Min Detector
 Region of inadequate image quality will be detected and
removed.
His (gray level)
Pr (gray level)
Min:- Pr ≥ Val1*Siz
Max:- Prmax ≥ Val2*Siz
Min≤ Pixel value ≥Max
Min≤ Pixel value ≥Max

14. Ratio Detector
 Extract the blurred regions from the retinal image.
퐑퐞퐝 + 퐆퐫퐧 < 퐓퐡퐫ퟏ
퐑퐞퐝 + 퐆퐫퐧 ≤ 퐓퐡퐫ퟐ퐚퐧퐝
퐆퐫퐧
퐑퐞퐝
≥ 퐓퐡퐫ퟑ퐚퐧퐝
퐆퐫퐧/퐑퐞퐝 ≤ 퐓퐡퐫ퟒ

15. Seed Filling Algorithm
 Remove the areas of white patches that may appear in the
resulted binary image, which are considered as poor image
quality areas.

16. Localization of ROI
 Allocate the actual region in the retinal image and flag its
pixels from other areas pixels in the ocular fundus image.
퐆퐚퐩퐬 퐅퐢퐥퐥퐢퐧퐠 퐚퐧퐝 퐍퐨퐢퐬퐞 퐑퐞퐦퐨퐯퐚퐥 퐄퐝퐠퐞 퐒퐦퐨퐨퐭퐡퐢퐧퐠
퐑퐞퐦퐨퐯퐢퐧퐠 퐨퐟 퐒퐦퐚퐥퐥 퐀퐫퐭퐢퐟퐢퐜퐢퐚퐥
퐆퐚퐩퐬 퐚퐧퐝 퐏퐨퐫퐞퐬

17. Allocate the Most Informatic
Color Band
 Green Channel image shows better contrast than the red
channel. It is observed that the anatomical and pathalogy
features appears most contrasted in green channel in RGB
image.

18. Allocate the Most Informatic
Color Band
50000
45000
40000
35000
30000
25000
20000
15000
10000
5000
0
1
16
31
46
61
76
91
106
121
136
151
166
181
196
211
226
241
256
35000
30000
25000
20000
15000
10000
5000
0
1
17
33
49
65
81
97
113
129
145
161
177
193
209
225
241
Contrast Stretching
Dif Between
Adjacent Pixels
Energy of Dif

19. Detection of Normal
Features (Optic Disk)
 OD is a bright yellowish disk within the retinal image. It is the
spot on the retina where the optic nerve and blood vessels
enter the eye.
 Specify the image, where it is for the left or right side.
 Cup the disk ratio is commonly used to assess the glaucoma
disease.
 Location of OD can be a target point for identify the
position of the macula.
OD is masked when detection of exudates to prevent the
false positive.

20. Detection of Normal
Features (Optic Disk)
 Locating the Optic Disk.
Thresholding Process Seed Filling Algorithm Circle Equation Mask Locating the OD

21. Detection of Normal
Features (Optic Disk)
 Accurate Localization of the Optic Disk.
Non-linear
Gamma Mapping
Seed Filling
Algorithm
Determine center
point
Circle Equation
Mask
Localized the Optic
Disk

22. Detection of Normal
Features (Macula & Fovea)
 Macula, is another part of the main components of the retina.
In a color retinal image, it appears roughly in the center of the
retina as darker small yellowish area adjacent to the optic disk
about (4.5 mm in diameter).
 Fovea is the central part of the region of the macula.
 It is vital to allocate the macular region.
 By localization the fovea, occurrence of the maculopathy can
be determined in the whole macular region.

23. Detection of Normal
Features (Macula & Fovea)
 Locating the Macula.

24. Detection of Normal
Features (Macula & Fovea)
 Locating the Macula and its center (Fovea).
Non-Linear
Gamma Mapping
Thresholding
Process
Seed Filling
Algorithm
Locating the
Fovea

25. Detection of Abnormal
Features (Exudates)
 Exudates is a fluid rich in fat, leaks out of diseased
vessels can deposited in the macular region leading to
the visual distortion.
 The common feature in hard
and soft exudates lesions is
that they both appear as
brighter areas relative to
their neighborhood.

26. Detection of Abnormal
Features (Exudates)
 Non-Linear Gamma Mapping.
 Max Filter (Dawn-Sampling).
 Local Thresholding.
 Remove Optic Disk.
 Up-Sampling
 Seed Filling Algorithm
 Classify the Exudates using brightness
and size Feature
3 5
2 6
6
x,y x+1,y x+2,y
x,y+1 x+1,y+1 x+2,y+1
x,y+2 x+1,y+2 x+2,y+2
2x,2y 2x+1,y
2x,2y+1 2x+1,2y+1

27. Determining the Severity
Level of Diabetic Maculopathy
 To automatic grading of diabetic maculopathy severity
level, the macular regions are divided into three circular
areas R1, R2 and R3 centered at fovea. Where R1 is
represent the sever region, while R2 for the moderate
region and R3 for the mild region.
Severity level Hard and Soft Exudates
R1 R2 R3
Sever Present Present/Absent Present/Absent
Moderate Absent Present Present/Absent
Mild Absent Absent Present

28. Determining the Severity
Level of Diabetic Maculopathy
Normal Mild Stage
Moderate Stage Sever Stage

29. Results
 The proposed system is tested om a publically available
datasets of color retinal image DIARETDB0 which contains
130 retinal image with size 1500×1152.
 96.92% accuracy rate in detecting of fundus image region
(background elimination).

30. Results
 97.67% accuracy rate in detecting the regions of poor image
quality in the localized region of interest.

31. Results
 93.49% accuracy rate in detecting optic disk. The Sensitivity
and specificity of detection achieved 92.68% and 100%,
respectively.

32. Results
 94.69% accuracy rate in detecting macula region. The
Sensitivity and specificity of detection achieved 94.17% and
100%, respectively.
 100% accuracy rate in detecting fovea.

33. Results
 87.62% accuracy rate in detecting optic disk. The Sensitivity
and specificity of detection achieved 88.46% and 86.66%,
respectively.

34. Thanks For Your
Attention!