A Novel Multi-Orientation Kernel for Retina Vessel Detection

Abstract

Retina is a thinnest tissue comprises of millions of blood vessels. It plays a vital role in the human eye that carries the visual signals to the brain for the interpretation. Any damage to the blood vessels in the retina causes serious issues related to the vision and it leads to the chronicle eye diseases like glaucoma, macular degeneration, diabetic retinopathy etc. Diabetic Retinopathy (DR) is a threatening disease among the diabetic patients in the recent years. Damage to the blood vessels causes DR. As the number of diabetic patients are comparatively high these days, it has become mandatory for the development of accurate system for segmenting the blood vessels in the retina which will reduce the work load of ophthalmologists to a greater extent. In this work we propose a novel technique named Multi-Orientation Kernel (MOK) for blood vessel detection. We also propose a framework for segmentation of retina blood vessels which follows the sequence of steps such as preprocessing, blood vessel extraction using proposed kernel (MOK) and refinement usingActive Contour method. This proposed method is tested on DRIVE and CHASE_DB1 dataset. The proposed algorithm produced 95% of accuracy on DRIVE datasetand 96% of accuracy on CHASE_DB1 dataset respectively. The performance of proposed approach is compared with few existing techniques.