Detection of diabetic retinopathy lesions in color retinal images

Advances in medical device technology have resulted in a plethora of devices that sense, record, transform and process digital data. Images are a key form of diagnostic data, and many devices have been designed and deployed that capture high-resolution in-vivo images in different parts of the spectrum. Computers have enabled complex forms of reconstruction of cross-sectional/ 3D structure (and temporal data) non-invasively, by combining views from multiple projections. Images thus present valuable diagnostic information that may be used to make well-informed decisions. Computer aided diagnosis is a contemporary exploration to apply computers to process digital data with the aim of assisting medical practitioners in interpreting diagnostic information. This thesis takes up a specific disease: diabetic retinopathy, which has visual characteristics manifesting in different stages. Image analysis and pattern recognition have been used to design systems with the objective of detecting and quantifying the extent. The quantitative information can be used by the practitioner to stage the disease and plan treatment, track drug efficacy, or make decisions on course of treatment or prognosis. The generic task of image understanding is known to be computationally ill-posed. However adding domain constraints and restricting the size of the problem make it possible to attempt solutions that are useful. Two basic tasks in image understanding : detection and segmentation, are used. A system is designed to detect a type of vascular lesion called microaneurysm, which appear in the retinal vasculature at the advent of diabetic retinopathy. As the disease progresses it manifests visually as exudative lesions, which are to be segmented, and a system has been developed for the same. The developed systems are tested with image datasets that are in the public domain, as well as a real dataset (from a local speciality hospital) collected during the course of the research, to compare performance indicators against the prior art and elicit better understanding of the factors and challenges involved in creating a system that is ready for clinical use.