Blood Vessel Detection using Modified Multiscale MF-FDOG Filters for Diabetic Retinopathy

TL;DR

This paper introduces a modified multiscale MF-FDOG filter approach for blood vessel detection in retinal images, improving accuracy and reducing false positives in diabetic retinopathy screening.

Contribution

It proposes a novel modified matched filter with the first derivative of Gaussian, enhancing blood vessel segmentation accuracy over existing methods.

Findings

Higher detection accuracy on DRIVE database

Reduced false detection rate

Effective segmentation of retinal blood vessels

Abstract

Blindness in diabetic patients caused by retinopathy (characterized by an increase in the diameter and new branches of the blood vessels inside the retina) is a grave concern. Many efforts have been made for the early detection of the disease using various image processing techniques on retinal images. However, most of the methods are plagued with the false detection of the blood vessel pixels. Given that, here, we propose a modified matched filter with the first derivative of Gaussian. The method uses the top-hat transform and contrast limited histogram equalization. Further, we segment the modified multiscale matched filter response by using a binary threshold obtained from the first derivative of Gaussian. The method was assessed on a publicly available database (DRIVE database). As anticipated, the proposed method provides a higher accuracy compared to the literature. Moreover, a lesser false detection from the existing matched filters and its variants have been observed.