Blood Vessel Detection using Modified Multiscale MF-FDOG Filters for Diabetic Retinopathy
This work addresses early detection of diabetic retinopathy to prevent blindness, but it is incremental as it builds on existing matched filter techniques.
The paper tackled the problem of false detection in blood vessel segmentation for diabetic retinopathy screening by proposing a modified multiscale matched filter with first derivative of Gaussian, achieving higher accuracy on the DRIVE database compared to existing methods.
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.