Comparison of blood vessel detection techniques in low quality and pathological retinal images

Abstract

An automatic blood vessel segmentation is considered as important due to the presence of similar properties between blood vessels and the early stage pathologies (i.e. Microaneurysm and hemorrhages) in retinal diseases [1,2]. Blood vessel segmentation is considered as a challenging task, if a retinal images having inadequate contrast, lightning variations, and different pathologies like exudates. In literature, there is a number of blood vessel segmentation algorithms exist, but they are not compared on similar datasets and thus, it is difficult to choose which one is best. In this paper, we have implemented five different blood vessel segmentation techniques based on Gaussian matched filter [4], edge based techniques [5,8] (Sobel, Prewitt and Kirsch) and entropy based thresholding [6] technique and compare them on similar datasets with low quality and pathological retinal images. Secondly, in low quality images, most of the pixels have lost their color intensity information and considered as noise. In order to retrieve their color information of pixels, initial pre-processing is needed. Thus, a different pre-processing approach includes mask generation and contrast enhancement to retrieve the pixel information are proposed in retinal images and compared. The results obtained are verified broadly on two publicly available standard datasets DIARETDB0 and STARE dataset comprises of 211 retinal images.