Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation

Roberto Annunziata (Lead / Corresponding author), Andrea Garzelli, Lucia Ballerini, Alessandro Mecocci, Emanuele Trucco

Research output: Contribution to journalArticle

67 Citations (Scopus)

Abstract

Accurate vessel detection in retinal images is an important and difficult task. Detection is made more challenging in pathological images with the presence of exudates and other abnormalities. In this paper we present a new unsupervised vessel segmentation approach to address this problem. A novel inpainting filter, called Neighbourhood Estimator Before Filling (NEBF), is proposed to inpaint exudates in a way that nearby false positives are significantly reduced during vessel enhancement. Retinal vascular enhancement is achieved with a multiple-scale Hessian approach. Experimental results show that the proposed vessel segmentation method outperforms state-of-the-art algorithms reported in the recent literature, both visually and in terms of quantitative measurements, with overall mean accuracy of 95.62% on the STARE dataset, and 95.81% on the HRF dataset.

Original languageEnglish
Pages (from-to)1129-1138
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume20
Issue number4
Early online date1 Jun 2015
DOIs
Publication statusPublished - 6 Jul 2016

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