Retinal vessel segmentation using a strip wise classification approach with grid search-based parameter selection
by Mahua Nandy Pal; Minakshi Banerjee
International Journal of Computational Vision and Robotics (IJCVR), Vol. 12, No. 2, 2022

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.

Online publication date: Mon, 28-Feb-2022

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