Fuzzy based image edge detection algorithm for blood vessel detection in retinal images

Abstract

We developed a contour detection based image processing algorithm based on Mamdani (Type-2) fuzzy rules for detection of blood vessels in retinal fundus images. The method uses the green channel data from eye fundus images as input, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, and median filter for background exclusion. The Mamdani (Type-2) fuzzy rules applied on image gradient value are used for edge detection. The results of experiments on the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE) and CHASEdb datasets show the applicability of the proposed method as a flexible approach which can be adapted to numerous edge detection/contour based applications. We achieved an accuracy of 0.865 for STARE dataset, an accuracy of 0.939 for the DRIVE dataset, and the accuracy of 0.950 for the ChaseDB dataset. In relation to works of other authors, our method offered a similar performance, but it offers an improved dynamics and flexibility in formulation of the linguistic threshold criteria, which can be a leading factor in design of image processing systems with dynamic and flexible rules, such as Type-2 fuzzy rules would allow, offering an interesting alternative to currently widespread deep learning applications.