2. CLASSIFICATION OF DIABETIC MACULAR
EDEMA USING COLORED FUNDUS IMAGE
PRESENTED BY:
MUHAMMAD ZUBAIR
SUPERVISED BY:
Dr. SHOAB AHMED KHAN

3. GUIDANCE AND EVALUATION COMMITTEE
 GEC Members
 Dr. Ubaid Ullah Yasin (Co-Supervisor)
(Asst. Prof. AFPGMI, Eye specialist AFIO)
 Dr. Aasia Khanum
 Dr. Ali Hassan

4. AGENDA
 Motivation and problem statement
 Introduction
 Literature review
 Proposed System
 Evaluation and Results
 Conclusion and Future work
 References

5. MOTIVATION AND PROBLEM
STATEMENT

7. MOTIVATION
 Diabetes Mellitus is a fast growing global disease
 In 2007 survey by International Diabetic Federation
(IDF) Pakistan was at 7th in top 10 countries having
diabetic population [1]
 This number may rise from 6.9 million in 2007 to
11.5 million in 2025 by IDF [1], almost double

8. STATISTICAL CHART BY IDF
2007 2025
Country Persons
(millions)
Country Persons
(millions)
1 India 40.9 1 India 69.9
2 China 39.8 2 China 59.3
3 USA 19.2 3 USA 25.4
4 Russia 9.6 4 Brazil 17.6
5 Germany 7.4 5 Pakistan 11.5
6 Japan 7.0 6 Mexico 10.8
7 Pakistan 6.9 7 Russia 10.3
8 Brazil 6.9 8 Germany 8.1
9 Mexico 6.1 9 Egypt 7.6
10 Egypt 4.4 10 Bangladesh 7.4

9. MOTIVATION …
 DME can lead to complete irreversible blindness
 Early stage detection is rare
 Doctor to patient ratio is very low in Pakistan
 Screening is hectic in populated areas
 Sparse resources in rural areas

10. PROBLEM STATEMENT
 To develop an automated CAD system to help
ophthalmologists in mass screening by
 Identification of abnormalities (exudates),
 Finding out their exact location and area within
the macular region,
 Stage classification of the disease

11. PUBLICATIONS
 CONFERENCES
 “Classification of Diabetic Macular Edema and Its Stages Using
Color Fundus Image” (Registered and to be presented in 2013 3rd
International conference on Signal, Image Processing and
Applications (ICSIA))
 “Automated Detection of Optic Disc for the Analysis of Retina
Using Color Fundus Image” (Accepted in 2013 IEEE International
conference on Imaging Systems And Techniques (IST))
 “Automated Segmentation of Exudates Using Dynamic
Thresholding in Retinal Photographs” (Accepted in 16th IEEE
International Multi Topic Conference (INMIC) 2013)
 JOURNALS
 “Automated Grading of Diabetic Macular Edema Using Colored
Fundus Photographs” (Submitted in Journal of Digital Imaging)

12. INTRODUCTION

13. HUMAN EYE
 Retinal layer of human eye comprises:
o Optic Disc (Head of optic nerve)
o Macula (Central portion of retina)
o Fovea (Center of macula)
o Network of Blood vessels

14. Blood Vessels
Macula
Fovea
Optic Disc
COLORED FUNDUS IMAGE

15. DIABETIC MACULAR EDEMA
 DME is the swelling of the macula
 Leaked fluid accumulates on the retina
 Leakage within the macula causes swelling

16. SCREENING TESTS FOR DME
 Fundus Fluorescein Angiography (FFA)
 Retinal image acquisition process
 2D colored and red free images
 Optical Coherence Tomography (OCT)
 3D high resolution image acquisition process
 Provides a cross-sectional image
 Used as optical biopsy by the ophthalmologists

17. FFA IMAGE ACQUISITION PROCESS

18. FFA COLORED IMAGE

19. OCT IMAGE ACQUISITION PROCESS

20. OCT IMAGE

21. LITERATURE REVIEW

22. Aquino et al.*
 Detect OD using edge detection
 Circular Hough transform
 Feature extraction
 Morphological operations
 Accuracy achieved was 86%
A. Aquino, M. E. G. Arias and D. Marin, “Detecting the Optic Disc Boundary in Digital Fundus Images Using
Morphological, Edge Detection and Feature Extraction Techniques,” IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol.
29, No. 11, pp.1860-1869, 2010

23. Siddalingaswamy et al.*
 Exudates detection using clustering
 Morphological techniques
 Exudates location based severity level
 Sensitivity achieved 95% and specificity 96%
 No publically available database used
 Use only 148 local fundus images
P.C. Siddaligaswamy, K. G. Prabhu, “Automatic Grading of Diabetic Maculopathy Severity Levels”, Proceedings of
2010 International Conference on Systems in Medicine and Biology, pp. 331-334, 2010

24. Lim et al.*
 Segmentation of exudates using watershed transform
 External and internal markers used
 Classification done as normal, stage1 and stage2
 Use only 88 images of MESSIDOR
 Sensitivity and Specificity achieved 80.9%, 90.2%
respectively
 Accuracy achieved was 85.2 percent
S.T. Lim, W.M.D.W. Zaki, A. Hussain, S.L. Lim, S. Kusalavan, “Automatic Classification of Diabetic Macular Edema in Digital Fundus
Images", 2011 IEEE Colloquium on Humanities, Science and Engineering (CHUSER), pp. 265-269, 2011.

25.  Giancardo et al.*
 Unsupervised technique for exudates segmentation
 Background estimation used
 Image normalization used
 8 neighbor connectivity used
 Used local database images
 No Stage classification was done
L. Giancardo, F. Meriaudeau, T.P. Karnowski, K. W. Tobin Jr, E. Chaum, MD, “AUTOMATIC RETINA EXUDATES SEGMENTATION
WITHOUT A MANUALLY LABELLED TRAINING SET”, IEEE Transactions on Medical Imaging, Vol. 21, No. 5, pp. 1396-1400, 2011

26. PROPOSED SYSTEM

27. PROPOSED SYSTEM
 Preprocessing of image
 Optic disc elimination
 Dynamic Thresholding
 All possible exudates detection
 Classification of the stage

28. FLOW CHART OF PROPOSED TECHNIQUE
Input Colored Fundus Image
Resizing of Image
Green Component
Preprocessing
OD Removal
Fovea
localization
Grayscale
Classifier

30. Input Colored Image
Green Channel of Image
Contrast Limited Adaptive Histogram Equalization
Contrast Stretching Transform
PREPROCESSING OF IMAGE

31. PREPROCESSING OF IMAGE
CLAHE
 Contrast Limited Adaptive Histogram Equalization
(CLAHE)
 To make the components visually distinct
 Distinction of foreground objects from background
 CLAHE used iteratively to get the desired result

32. Colored image 1st CLAHE
2nd CLAHE
Green component

33. PREPROCESSING OF IMAGE
CONTRAST STRETCHING TRANSFORM
 Expands the range of intensity of the image
 Range span is defined by the user
 Improves the overall contrast of the image
 Increases the contrast b/w dark and lights
 Stretches the intensity domain histogram

34. Colored image 1st CLAHE
2nd CLAHE
Green component
Contrast Stretching

35. RESULTS OF PREPROCESSING
Green Component 1st CLAHE 2nd CLAHE Contrast Stretching

36. OPTIC DISC (OD) ELIMINATION
 OD becomes prominent after preprocessing
 Detection on basis of highest intensity value pixels
 Extended minima transform applied
 Detect all candidate OD regions
 Morphological operations to extract real OD

37. OD EXTRACTION FLOW CHART

38. OPTIC DISC (OD) ELIMINATION
EXTENDED MINIMA TRANSFORM
 Computes the regional minima
 Regional minima are connected components of
pixels with constant intensity value
 The external boundary pixels have high value
 8-neighbor connectivity used
 Selects all possible candidate regions for OD

39. 8 neighbor connectivity
(x-1 , y+1) (x , y+1) (x+1 , y+1)
(x-1 , y) (x , y) (x+1 , y)
(x-1 , y-1) (x , y-1) (x+1 , y-1)

40. Contrast Stretching Transform Extended minima Transform
EXTENDED MINIMA TRANSFORM RESULT

41. MORPHOLOGICAL OPERATIONS
 Morphological Erotion
 To remove all non OD candidate regions
 Structuring element (SE) having size less than OD size
 SE chosen is of the size one fourth of the radius of OD
 Erotion also shrinks the actual OD region

42. MORPHOLOGICAL OPERATIONS
 Morphological Dilation
 To get the actual size of OD after erotion
 SE is of the size approx. equal to the radius of OD
 Dilation restores the size and shape of actual OD
 False OD are no more in the image

43. PREPROCESSING AND OD ELIMINATION

44. RESULTS OF OD ELIMINATION

45. DYNAMIC THRESHOLDING
 Fundus images are taken in different illumination
 All images have different intensity levels
 Intensity based parameters chosen for dynamic
thresholding
 Mean and standard deviation of the image is used
 Threshold value is set using these parameters

46. CALCULATION OF THRESHOLD VALUE
USING MEAN AND STANDARD DEVIATION
Mean Standard deviation Threshold value
30.05 33.35 2.8
31.25 34.6 2.8
31.25 35.4 2.8
31.9 35.7 2.9
32.45 36.2 2.9
32.55 35.92 3.0
32.84 35.96 3.0
35.08 38.6 3.2
35.09 40.2 3.3
35.49 39.38 3.3
35.85 39.40 3.3
35.95 39.46 3.3
37.15 41.87 3.4
37.35 41.56 3.4
38.57 42.27 3.4
41.00 46.65 3.6
41.22 47.1 3.65
41.53 47.06 3.65
43.10 48.48 3.7
43.07 48.05 3.75
43.27 47.71 3.75
44.58 49.58 3.9
45.56 51.30 3.95
48.20 53.50 4.3
49.4 54.51 4.4

47. 2.8
3.0
3.9
3.6
4.4
4.1
3.4

48. DETECTION OF EXUDATES
 Exudates are bright lesions
 Bright yellowish spots in colored fundus image
 OD free image is used for exudates detection
 Segmentation is done for exudates detection

49. RESULTS OF EXUDATES DETECTION

50. ETDRS STAGING CRITERIA
 Early Treatment Diabetic Retinopathy Study
(ETDRS)
 Classification of DME is based on the standard
criteria set by ETDRS
 Severity level depends upon size and location of
abnormality
 Reference point is the center of macula

51. CIRCULAR AREA FOR STAGING

52. RESULTS OF EXUDATES DETECTION

53. CLASSIFICATION OF STAGES
 Normal Condition
 No abnormality is found
 Less Significant Stage
 Abnormality found outside the circular area of 1 Disc
Diameter 1DD radius but within 2DD from the fovea
 Moderate Stage
 Abnormality found beyond 1/3DD (radius) but within 1DD
of circle
 Severe Stage
 Abnormality found within 1/3DD circular area from the
center of fovea region

54. STAGE CLASSIFICATION TABLE
Stage Description
Normal No abnormality is found
Severe
Abnormality found within 1/3DD radius circular
area from the center of fovea region
Moderate
Abnormality found beyond 1/3DD radius but
within 1DD radius of circle
Less
significant
Abnormality found outside the circular area of
1DD radius but within 2DD radius from the
fovea region
1Disc Diameter (DD) = 1500µm (1.5mm)

55. CLASSIFICATION OF STAGES
Colored input image Normal Case
Colored input image Less Significant Stage

56. Colored input image
Colored input image Moderate Stage
Severe Stage
CLASSIFICATION OF STAGES…

57. RESULTANT MESSAGES DISPLAYED AFTER THE
CLASSIFICATION OF STAGES
 If no exudates are found in the image or the abnormality is found outside the
2DD circle, it is declared as normal case. The message will be displayed “No
exudates found: Normal eye”.
 If the exudates are found within the outermost 2DD circle the stage is called
insignificant. The message will be displayed “warning: exudates found within
the outermost circle, insignificant stage”.
 If the exudates are found in both outermost 2DD and the middle circle 1DD
circle, it is declared as moderate stage. The message will be displayed
“warning: exudates found within the outermost circle and middle circle,
moderate stage”.
 If the exudates are found in the middle circle only 1DD circular area it is known
as moderate stage. The message will be displayed “warning: exudates found
within the middle circle, moderate stage”.
 If the abnormality (exudates) found within both the middle circle 1DD circular
area and the innermost 1/3DD circle the stage is known as severe stage. The
message will be displayed “warning: exudates found within the middle circle
and the innermost circle, severe stage”.
 If the exudates are found within the innermost 1/3DD circle the stage is called
severe stage. The message displayed in this case “warning: exudates found in
the innermost circle, severe stage”.

58. EVALUATION AND RESULTS

59. RESULTS
 Performance metrics
 Sensitivity
 Specificity
 Accuracy
 Sensitivity = TP / (TP + FN)
 Specificity = TN / (TN + FP)
 Accuracy = (TP+ TN) / (TP + FN + TN + FP)
 TP for True Positive (Correctly Identified)
 TN for True Negative (Correctly Rejected)
 FP for False Positive (Incorrectly Identified)
 FN for False Negative (Incorrectly Rejected)

60. CLASSIFICATION RESULTS
Total no. of images Normal
Cases
Insignificant Moderate Severe
1200 869 122 136 73
Proposed Method
Sensitivity (%) Specificity (%) Accuracy (%)
98.27 96.58 96.54

61. EXUDATES DETECTION RESULT
Author Sens(%) Spec(%) Accu(%)
Deepak et al. 100 97 81
Giancardo et al. - - 89
Proposed technique 98.73 98.25 97.62

62. GRAPHICAL RESULT FOR EXUDATES DETECTION
0
20
40
60
80
100
120
Deepak et al Giancardo et al. Proposed technique
Sensitivity
Specificity
Accuracy

63. Author Sensitivity (%) Specificity (%) Accuracy (%)
Lim et al. 80.9 90.2 85.2
Deepak et al. 95 90 -
Proposed Method 98.27 96.58 96.54
CLASSIFICATION OF DME RESULT

64. GRAPHICAL RESULT FOR CLASSIFICATION
OF DME
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Lim et al. Deepak et al. Proposed technique
Sensitivity
Specificity
Accuracy

65. CONCLUSION AND FUTURE WORK

66. CONCLUSION
 Improved performance of the system in terms of
 True localization of optic disc and its removal
 Detection of exudates and determining their position
 Classifying stage of the disease
 The System provide accurate results using less but
effective features of the input image
 Efficient system for identifying and classifying the DME
 The system can be used practically in diagnostic
environment with a sound reliability

67. FUTURE WORK
 Classification of DME using OCT (3D) images
 Fusion of FFA and OCT results for easy and better
diagnosis
 Detection of soft exudates/drusens in fundus
images

68. REFERENCES

69. REFERENCES
 [1] International Diabetic Federation Atlas. 2006 showing prevalence of diabetes in 2007
and future projection for 2025. Available from: http://www.idf.org/diabetesatlas. (Updated
in 2012)
 [2] A. Aquino, M. E. G. Arias and D. Marin, “Detecting the Optic Disc Boundary in Digital
Fundus Images Using Morphological, Edge Detection and Feature Extraction
Techniques,” IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol. 29, No. 11, pp.1860-
1869, 2010
 [3] K. S. Deepak and J. Sivaswamy, “Automatic Assessment of Macular Edema From
Color Retinal Images", IEEE Transactions on Medical Imaging, Vol. 31, No. 3, pp. 766-
776, 2012
 [4] P.C. Siddalingaswamy, K. G. Prabhu, “Automatic Grading of Diabetic Maculopathy
Severity Levels”, Proceedings of 2010 International Conference on Systems in Medicine
and Biology, pp. 331-334, 2010
 [5] L. Giancardo, F. Meriaudeau, T.P. Karnowski, K. W. Tobin Jr, E.
Chaum, MD, “AUTOMATIC RETINA EXUDATES SEGMENTATION WITHOUT A
MANUALLY LABELLED TRAINING SET”, IEEE Transactions on Medical Imaging, Vol.
21, No. 5, pp. 1396-1400, 2011
 [6] Klein R, Klein BE, Moss SE, Davis MD, DeMets DL. The Wisconsin epidemiologic
study of diabetic retinopathy. IV. Diabetic macular edema Ophthalmology 1984; 91(12):
1464-74

70. REFERENCES…
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Exudate from Retinal Images Using Clustering Algorithm,” Intelligent Systems and
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 [8] Shijian Lu, “Automatic Optic Disc Detection using Retinal Background and Retinal
Blood Vessels,” Biomedical Engineering and Informatics (BMEI), vol.1, pp.141-145, Oct.
2010
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Anal., Vol. 4, pp. 73-91, 2000
 [10] Hoover, V. Kouznetsova and M. Goldbaum, “Locating blood vessels in retinal images
by piecewise threshold probing of a matched filter response”, IEEE Trans Med Imag., Vol.
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 [11] Sumathy.B, and Dr Poornachandra S, “Retinal blood vessel segmentation using
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 [12] S. Sekhar, W.A. Nuaimy, A.K. Nandi, “Automatic Localisation of optic disc and fovea
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fovea in human retinal fundus images" In Emerging Technologies (ICET), 2012
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 [14] U. R. Acharya, C. K. Chua, E. Y. K. Ng, W. Yu, C. Chee, “Application of Higher Order
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71. REFERENCES…
 [15] K. S. Deepak and J. Sivaswamy, “Automatic Assessment of Macular Edema From
Color Retinal Images", IEEE Transactions on Medical Imaging, Vol. 31, No. 3, pp. 766-
776, 2012
 [16] Jack J Kanski and Brad Bowling, “Clinical Ophthalmology A Systematic Approach,”
seventh edition, May 2011
 [17] MESSIDOR: http://messidor.crihan.fr/index-en.php (visited: 19 Feb 2013)
 [18] Xiaolu Zhu and Rangaraj M. Rangayyan, “Detection of the Optic Disc in Images of
the Retina Using the Hough Transform,” IEEE Conf. on Engineering in Medicine and
Biology Society, pp.3546-3549, Aug. 2008