An Efficient Blood Vessel Detection Algorithm For Retinal Images Using Local Entropy Thresholding

Diabetic retinopathy is one of the serious eye diseases that can cause blindness and vision loss. 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 color retinal images of a patient acquired from digital fundus camera. The present study is aimed at developing an automatic system for the extraction of normal and abnormal features in color retinal images. Prolonged diabetes causes micro-vascular leakage and micro-vascular blockage within the retinal blood vessels. Filter based approach with morphological filters is used to segment the vessels. The morphological filter are tuned to match that part of vessel to be extracted in a green channel image. To classify the pixels into vessels and non vessels local thresholding based on gray level co-occurrence matrix is applied. The performance of the method is evaluated on two publicly available retinal databases with hand labeled ground truths. The performance of retinal vessels on drive database, sensitivity 86.39%, accompanied by specificity of 91.2%. While for STARE database proposed method sensitivity 92.15 % and specificity 84.46%. The system could assist the ophthalmologists, to detect the signs of diabetic retinopathy in the early stage, for a better treatment plan and to improve the vision related quality of life. Keywords— Vessel segmentation, Morphological filter, Image Processing, Diabetic Retinopathy .