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Thesis presentation

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  • Hi Zubair: Very nice presentation. I have a question about the slide #34: Could you describe a little more about the first CLAHE and the second CLAHE?
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Thesis presentation

  1. 1. CLASSIFICATION OF DIABETIC MACULAR EDEMA USING COLORED FUNDUS IMAGE PRESENTED BY: MUHAMMAD ZUBAIR SUPERVISED BY: Dr. SHOAB AHMED KHAN
  2. 2. GUIDANCE AND EVALUATION COMMITTEE  GEC Members  Dr. Ubaid Ullah Yasin (Co-Supervisor) (Asst. Prof. AFPGMI, Eye specialist AFIO)  Dr. Aasia Khanum  Dr. Ali Hassan
  3. 3. AGENDA  Motivation and problem statement  Introduction  Literature review  Proposed System  Evaluation and Results  Conclusion and Future work  References
  4. 4. MOTIVATION AND PROBLEM STATEMENT
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. 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
  9. 9. 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)
  10. 10. INTRODUCTION
  11. 11. 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
  12. 12. Blood Vessels Macula Fovea Optic Disc COLORED FUNDUS IMAGE
  13. 13. DIABETIC MACULAR EDEMA  DME is the swelling of the macula  Leaked fluid accumulates on the retina  Leakage within the macula causes swelling
  14. 14. 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
  15. 15. FFA IMAGE ACQUISITION PROCESS
  16. 16. FFA COLORED IMAGE
  17. 17. OCT IMAGE ACQUISITION PROCESS
  18. 18. OCT IMAGE
  19. 19. LITERATURE REVIEW
  20. 20. 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
  21. 21. 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
  22. 22. 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.
  23. 23.  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
  24. 24. PROPOSED SYSTEM
  25. 25. PROPOSED SYSTEM  Preprocessing of image  Optic disc elimination  Dynamic Thresholding  All possible exudates detection  Classification of the stage
  26. 26. FLOW CHART OF PROPOSED TECHNIQUE Input Colored Fundus Image Resizing of Image Green Component Preprocessing OD Removal Fovea localization Grayscale Classifier
  27. 27. Input Colored Image Green Channel of Image Contrast Limited Adaptive Histogram Equalization Contrast Stretching Transform PREPROCESSING OF IMAGE
  28. 28. 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
  29. 29. Colored image 1st CLAHE 2nd CLAHE Green component
  30. 30. 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
  31. 31. Colored image 1st CLAHE 2nd CLAHE Green component Contrast Stretching
  32. 32. RESULTS OF PREPROCESSING Green Component 1st CLAHE 2nd CLAHE Contrast Stretching
  33. 33. 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
  34. 34. OD EXTRACTION FLOW CHART
  35. 35. 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
  36. 36. 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)
  37. 37. Contrast Stretching Transform Extended minima Transform EXTENDED MINIMA TRANSFORM RESULT
  38. 38. 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
  39. 39. 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
  40. 40. PREPROCESSING AND OD ELIMINATION
  41. 41. RESULTS OF OD ELIMINATION
  42. 42. 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
  43. 43. 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
  44. 44. 2.8 3.0 3.9 3.6 4.4 4.1 3.4
  45. 45. 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
  46. 46. RESULTS OF EXUDATES DETECTION
  47. 47. 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
  48. 48. CIRCULAR AREA FOR STAGING
  49. 49. RESULTS OF EXUDATES DETECTION
  50. 50. 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
  51. 51. 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)
  52. 52. CLASSIFICATION OF STAGES Colored input image Normal Case Colored input image Less Significant Stage
  53. 53. Colored input image Colored input image Moderate Stage Severe Stage CLASSIFICATION OF STAGES…
  54. 54. 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”.
  55. 55. EVALUATION AND RESULTS
  56. 56. 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)
  57. 57. 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
  58. 58. 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
  59. 59. GRAPHICAL RESULT FOR EXUDATES DETECTION 0 20 40 60 80 100 120 Deepak et al Giancardo et al. Proposed technique Sensitivity Specificity Accuracy
  60. 60. 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
  61. 61. 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
  62. 62. CONCLUSION AND FUTURE WORK
  63. 63. 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
  64. 64. 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
  65. 65. REFERENCES
  66. 66. 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
  67. 67. REFERENCES…  [7] G.S. Annie Grace Vimala, S. Kaja Mohideen, “Automatic Detection of Optic Disc and Exudate from Retinal Images Using Clustering Algorithm,” Intelligent Systems and Control (ISCO), pp.280-284, Jan. 2013  [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  [9] T. McInerney and D. Terzopoulos, T-snakes: Topology adaptive snakes", Med Image 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. 19, pp. 203-211, 2000  [11] Sumathy.B, and Dr Poornachandra S, “Retinal blood vessel segmentation using morphological structuring element and entropy thresholding”, Conf. on Computing Communication & Networking Technologies (ICCCNT), pp.1-5, July 2012  [12] S. Sekhar, W.A. Nuaimy, A.K. Nandi, “Automatic Localisation of optic disc and fovea in retinal images", 16th European Signal Processing Conference (EUSIPCO), 2008  [13] Asim, Khawaja Muhammad, A. Basit, and Abdul Jalil. "Detection and localization of fovea in human retinal fundus images" In Emerging Technologies (ICET), 2012 International Conference on, pp. 1-5. IEEE, 2012  [14] U. R. Acharya, C. K. Chua, E. Y. K. Ng, W. Yu, C. Chee, “Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages", Journal of Med Systems Vol. 32, pp. 481-488, 2008
  68. 68. 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

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