期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 20, 期 4, 页码 1129-1138出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2015.2440091
关键词
Exudates; inpainting; retina; vessel segmentation
类别
资金
- EU Marie Curie Initial Training Network (ITN) REtinal VAscular Modelling, Measurement And Diagnosis (REVAMMAD) [316990]
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 neighborhood estimator before filling, 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.
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