Automatic detection of retinal features for screening of diabetic retinopathy using image processing techniques

Abstract

The work in this thesis mainly focuses on the development of an automatic system, for the purpose of detecting anatomical and pathological features in colour retinal images, with its application to diagnosis of diabetic related eye diseases. Diabetes mellitus, a metabolic disorder, has become one of the rapidly increasing health threats both in India and worldwide. The complication of the diabetes associated to retina of the eye is diabetic retinopathy. A patient with the disease has to undergo periodic screening of eye. For the diagnosis, ophthalmologists use colour retinal images of a patient acquired from digital fundus camera. Limited number of specialist ophthalmologists in most of the countries motivates the need for computer based analysis of retinal images using image processing techniques. This could reduce the workload of ophthalmologists, also aid in diagnosis, to make measurements and to look for a change in lesions or severity of disease. The present study is aimed at developing an automatic system for the extraction of normal and abnormal features in colour retinal images. Prolonged diabetes causes micro-vascular leakage and micro-vascular blockage within the retinal blood vessels. Therefore segmentation of retinal vasculature is of primary interest in the detection of retinopathy. Filter based approach with a bank of Gabor filters is used to segment the vessels. The frequency and orientation of Gabor filter are tuned to match that of a part of vessel to be extracted in a green channel image. A set of 12 filters with different orientations in the range of 0 to 170 degrees are convolved with the image and only the maximum response at each pixel is retained. Filtering leaves the image with enhanced vessels compared to the background.