Implementation of Pre-processing and Efficient Blood Vessel Segmentation in Retinopathy Fundus Image

Abstract

The human retina is a light receptive tissue and its enormously rich in blood vessels for its high physiological stress and dysfunction of the retinal vasculature can effect from several diseases. Diabetic retinopathy is caused due to complications of diabetes, which can eventually develop new blood vessels at the back of the retina and it can lead to blur vision or loss of vision. This work describes the problems of retinopathy associated with diabetic patients and premature babies. We propose methods for the preprocessing and efficient segmentation method to support measurement of the openness of the MTA, including image enhancement techniques like morphological operations, efficient luminance component construction and bank of Gabor filters to segment retinal blood vessels. Finally an image cropping is used to separate inferior and superior part of this segmented image for the effective and detailed analysis of the vascular structure in the fundus eye images. Certain retinal disorders, if not detected in time, can cause serious problems like blur vision and blindness in patients. The implementation and the performance of the various edge detection methods like Canny, Sobel and Gabor filters are based on visual perception. It has been concluded that in case of natural images such as retinal fundus image a Gabor filter yielded better results in segmentation of blood vessels as compared to edge detection methods of Canny and Sobel.
DOI: 10.17762/ijritcc2321-8169.150664