Teamed with 2 students to research and implement the automation of diagnosis of Diabetic Retinopathy and co-ordinated with an Ophthalmologist to verify our implementation.
Responsibilities included MATLAB coding, algorithm testing, and product documentation.
• Automation in MATLAB involving retinal image analysis to help
Ophthalmologist increase the productivity and efficiency in a clinical
environment.
• Used Image Processing concepts such as Hough Transform, Bottom Hat
Transform, Edge Detection Technique and Morphological Operators.
Provided our algorithm and documentation to our research faculty advisor to enable him to continue this research to the next phase.
2. Start Project Flowchart Retinal Image acquisition from Ophthalmologist Image Processing and Automation algorithm in MATLAB Result comparison with that of Ophthalmologist Stop August 03,2010 2 Confidential Information
11. Start Project Flowchart Retinal Image acquisition from Ophthalmologist Image Processing and Automation algorithm in MATLAB Result comparison with that of Ophthalmologist Stop August 03,2010 5 Confidential Information
12. Image Processing and Automation algorithm Flowchart Start Retinal Image acquisition Retinal Image plane separation Identification of Optic Disk using Hough Transform Identification of Blood Vessels using Bottom Hat transform Detection of Microaneurysms using Bottom Hat transform and Morphological operators Stop 6 August 03,2010 Confidential Information
13. Retinal Image Plane Separation Red plane ( Optic Disk detection) Green plane ( Microaneurysms and Blood vessels detection) Blue plane August 03,2010 7 Confidential Information
14. Image Processing and Automation algorithm Flowchart Start Retinal Image acquisition Retinal Image plane separation Identification of Optic Disk using Hough Transform Identification of Blood Vessels using Bottom Hat transform Detection of Microaneurysms using Bottom Hat transform and Morphological operators Stop August 03,2010 8 Confidential Information
15.
16. Its shape is more or less circular, interrupted by outgoing vessels
17. Its size varies between different patients and is approximately 50 pixels in 576 x 768 color photographs9 Confidential Information August 03,2010
23. Falsely detected circular edges are eliminated by selecting the largest circleOD marked image Original image August 03,2010 10 Confidential Information
24. Image Processing and Automation algorithm Flowchart Start Retinal Image acquisition Retinal Image plane separation Identification of Optic Disk using Hough Transform Identification of Blood Vessels using Bottom Hat transform Detection of Microaneurysms using Bottom Hat transform and Morphological operators Stop 11 August 03,2010 Confidential Information
29. It is given by the formula: Bottom Hat image = (original image) – (closing image) August 03,2010 12 Confidential Information
30. Closing Image Original Image Bottom Hat transformed Image August 03,2010 13 Confidential Information
31.
32. Uses two thresholds to detect strong and weak edges
33.
34. It is used to highlight the longer & connected vessels, thus eliminating smaller thread like structures Thresholded Image August 03,2010 14 Confidential Information
35. BV Marked Image Original Image August 03,2010 15 Confidential Information
36. Image Processing and Automation algorithm Flowchart Start Retinal Image acquisition Retinal Image plane separation Identification of Optic Disk using Hough Transform Identification of Blood Vessels using Bottom Hat transform Detection of Microaneurysms using Bottom Hat transform and Morphological operators Stop August 03,2010 16 Confidential Information
37.
38. Candidate region possibly corresponding to MAs selectedCanny Edge Detected Image BV Thresholded Image Subtracted Image August 03,2010 17 Confidential Information
39. MA Detection Cont… Suitable threshold window selected for exact detection of MA on subtracted image Threshold Image Morphological operators used to find exact MAs on threshold image Detected MAs August 03,2010 18 Confidential Information
40. Original Image MAs marked image August 03,2010 19 Confidential Information
41. MATLAB algorithm Result August 03,2010 20 Confidential Information
42. sing All Detected Features August 03,2010 21 Confidential Information
43. Start Project Flowchart Retinal Image acquisition from Ophthalmologist Image processing and automation algorithm in MATLAB Result comparison with that of Ophthalmologist Stop August 03,2010 22 Confidential Information
44. Result comparison with that of Ophthalmologist MA s detected using MATLAB algorithm MAs marked by Ophthalmologist August 03,2010 23 Confidential Information
45.
46. FN is the abnormal sample classified as normal i.e. features which are MAs but not detected by the algorithm
47. FP is the number of negative outcomes i.e. features that are not MAs but detected as MAsAugust 03,2010 24 Confidential Information
53. Accuracy of the system can be further improved by considering broader and more enhanced specifications of the features like their inclination and connectivity to the neighboring blood vessels
54. Advanced types of DR like PDR, moderate NPDR and Diabetic Maculopathy can be diagnosed on similar lines
55. A high speed processor or better computer programming languageslike visual C++, java etc can be used to improve the processing speedAugust 03,2010 28 Confidential Information
56. THANK YOU 29 August 03,2010 Confidential Information
