Efficient Kernel based Matched Filter Approach for Segmentation of Retinal Blood Vessels

TL;DR

This paper introduces an efficient kernel-based matched filter method for retinal blood vessel segmentation, improving accuracy by using a more suitable kernel that better matches vessel profiles, validated on DRIVE and STARE datasets.

Contribution

A novel kernel-based matched filter approach with an optimized kernel for improved retinal vessel segmentation accuracy.

Findings

Achieved over 98% specificity on both datasets

Attained around 95% accuracy in vessel segmentation

Outperformed existing segmentation methods

Abstract

Retinal blood vessels structure contains information about diseases like obesity, diabetes, hypertension and glaucoma. This information is very useful in identification and treatment of these fatal diseases. To obtain this information, there is need to segment these retinal vessels. Many kernel based methods have been given for segmentation of retinal vessels but their kernels are not appropriate to vessel profile cause poor performance. To overcome this, a new and efficient kernel based matched filter approach has been proposed. The new matched filter is used to generate the matched filter response (MFR) image. We have applied Otsu thresholding method on obtained MFR image to extract the vessels. We have conducted extensive experiments to choose best value of parameters for the proposed matched filter kernel. The proposed approach has examined and validated on two online available DRIVE and STARE datasets. The proposed approach has specificity 98.50%, 98.23% and accuracy 95.77 %, 95.13% for DRIVE and STARE dataset respectively. Obtained results confirm that the proposed method has better performance than others. The reason behind increased performance is due to appropriate proposed kernel which matches retinal blood vessel profile more accurately.