Saliency driven vasculature segmentation with infinite perimeter active contour model

Date published

2017-02-22

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Volume Title

Publisher

Elsevier

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Type

Article

ISSN

0925-2312

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Citation

Zhao Y, Zhao J, Yang J, Liu Y, Zhao Y, Zheng Y, Xia L, Wang Y, Saliency driven vasculature segmentation with infinite perimeter active contour model, Neurocomputing, Vol. 259, 11 October 2017, pp. 201-209

Abstract

Automated detection of retinal blood vessels plays an important role in advancing the understanding of the mechanism, diagnosis and treatment of cardiovascular disease and many systemic diseases, such as diabetic retinopathy and age-related macular degeneration. Here, we propose a new framework for precisely segmenting retinal vasculatures. The proposed framework consists of three steps. A non-local total variation model is adapted to the Retinex theory, which aims to address challenges presented by intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The image is then divided into superpixels, and a compactness-based saliency detection method is proposed to locate the object of interest. For better general segmentation performance, we then make use of a new infinite active contour model to segment the vessels in each superpixel. The proposed framework has wide applications, and the results show that our model outperforms its competitors.

Description

Software Description

Software Language

Github

Keywords

Saliency, Retinex, Active contour, Vascular segmentation

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Attribution-NonCommercial-NoDerivatives 3.0

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