Analysis of Retinal Angiogram Images

B. R. Siva Chandra (homepage)

Diabetes is occuring in an ever increasing percentage of the human population. Though generally non- fatal, it can lead to diseases of other vital organs of the human body. Diabetic Retinopathy (DR) is one such disease which affects the human retina. If not treated in time, the affected patient can lose his/ her sight. With a growing number of patients affected with diabetes, the need is for fast and automatic com- puter aided tools which can aid in the diagnosis of DR. Currently, DR is diagnosed by a manual analysis of retinal angiogram images (RAIs). This process is tedious and depends on the subjective perception of the doctors and technicians. In this thesis, we propose a modular framework for computer aided analysis of RAIs which can be used to build analysis systems which can automatically detect diseases like the DR and assign an objective measure to the extent of the disease. The framework consists four independent modules: 1) The Pre-processing Module - For rectification of the problems and defects affecting a RAI; 2) The Structure Analysis Module - For extraction of the structure of the retina; 3) The Disease Analy- sis Module - For extracting the candidate regions affected by a particular disease; 4) The Classification Module - For classifying the candidate ‘disease-regions’ into true positives and false positives. Depend- ing on the desired output, one can choose to incorporate some or all of these modules into the analysis system.

Non-uniform illumination is a common problem affecting RAIs and needs to be addressed. A technique for correcting non-uniform illumination forms a part of the pre-processing module. In this thesis, a technique for illumination correction, which models the illumination effect as a multiplicative degradation, is presented.

The most important of the structural features of the retina are the blood vessels. Blood vessels can be detected by modeling them as topographic ridges. In this thesis, a novel curvature estimation technique is presented, using which a ridge detection algorithm is formulated for single scale as well as multiple scales.

DR leads to two different kinds of pathologies in the human retina. These are: a.) Microaneurysms, (MAs) and b.) Capillary Non-Perfusion (CNP). In this thesis, a novel curvature based technique for detection of MAs is presented. Likewise, a novel technique for segmentation of regions of CNP, from RAIs obtained using a laser camera, is presented. This segmentation technique uses a special property of the images obtained using a laser camera.

To showcase the proposed framework, a tool called the ‘CNP Analyser’that was developed is presented. This tool can detect the regions of CNP from RAIs obtained using a laser camera. The proposed illumination correction technique and the CNP segmentation technique are incorporated into this tool. A measure of the extent of CNP is derived using the percentage area of the regions of CNP.

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