Title: Retinal vessel segmentation using a strip wise classification approach with grid search-based parameter selection

Authors: Mahua Nandy Pal; Minakshi Banerjee

Addresses: CSE Department, MCKV Institute of Engineering, Howrah, West Bengal, India ' CSE Department, RCC Institute of Information Technology, Kolkata, West Bengal, India

Abstract: Blood vessel characteristics of retinal images can be utilised for early detection of diseases like diabetes, hypertension, glaucoma, etc. In case of abnormal retinal symptoms like neovascularisation and aneurysm, accurate extraction of local vessel is very significant. Challenges present in automatic vessel detection are varying vessel width, presence of optic disc, neovascularisation, exudates, aneurysm, haemorrhage and low contrast. This paper proposes an automated segmentation of retinal vasculature using Gabor filter bank, optimised on the basis of grid search over the whole parameter space, and a new strip wise classification approach. Tophat features and ridge information based on eigenvalues of Hessian matrix are also considered along with optimised Gabor features to capture vessels more precisely. Accuracy of about 95% in all the cases proves the efficiency of strip wise classification. Discriminative power of features increases when different sets of features are considered together.

Keywords: Gabor filter; Tophat transformation; ridge enhancement; strip-based classification.

DOI: 10.1504/IJCVR.2022.121187

International Journal of Computational Vision and Robotics, 2022 Vol.12 No.2, pp.194 - 218

Received: 11 Mar 2021
Accepted: 18 Apr 2021

Published online: 24 Jan 2022 *

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