Segmentation of Optic Disc and Blood Vessels in Retinal Images usingWavelets, Mathematical Morphology and Hessian-based Multi-scale Filtering

摘要

A digitized image captured by a fundus camera provides an effective, inexpensive and non-invasive resource for the assessment of vascular damage caused by diabetes, arterial hypertension, hypercholesterolemia and aging. These unhealthy conditions may have very serious consequence like hemorrhages, exudates, branch retinal vein occlusion, leading to the partial or total loss of vision capabilities. This study has focus on the computer vision techniques of image segmentation required for a completely automated assessment system for the vascular conditions of the eye. The study here presented proposes a new algorithm based on wavelets transforms and mathematical morphology for the segmentation of the optic disc and a Hessian based multiscale filtering to segment the vascular tree in color eye fundus photographs. The optic disc and vessel tree, are both essential to the analysis of the retinal fundus image. The optic disc can be identified by a bright region on the fundus image, for its segmentation we apply Haar wavelets transform to obtain the low frequencies representation of the image and then apply mathematical morphology to enhance the segmentation. The tree vessel segmentation is achieved using a Hessian-based multi-scale filtering that, based on its second order derivatives, explores the tubular shape of a blood vessel to classify the pixels as part, or not, of a vessel. The proposed method is being developed and tested based on the DRIVE database, which contains 40 color eye fundus images.