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Neural Computing and Applications
Published in: Neural Computing and Applications 8/2018

11-02-2017 | New Trends in data pre-processing methods for signal and image classification

Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation

Published in: Neural Computing and Applications | Issue 8/2018

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Abstract

Recently, numerous research works in retinal-structure analysis have been performed to analyze retinal images for diagnosing and preventing ocular diseases such as diabetic retinopathy, which is the first most common causes of vision loss in the world. In this paper, an algorithm for vessel detection in fundus images is employed. First, a denoising process using the noise-estimation-based anisotropic diffusion technique is applied to restore connected vessel lines in a retinal image and eliminate noisy lines. Next, a multi-scale line-tracking algorithm is implemented to detect all the blood vessels having similar dimensions at a selected scale. An openly available dataset, called “the STARE Project’s dataset,” has been firstly utilized to evaluate the accuracy of the proposed method. Accordingly, our experimental results, performed on the STARE dataset, depict a maximum average accuracy of around 93.88%. Then, an experimental evaluation on another dataset, named DRIVE database, demonstrates a satisfactory performance of the proposed technique, where the maximum average accuracy rate of 93.89% is achieved.
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Metadata
Title
Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation
Publication date
11-02-2017
Published in
Neural Computing and Applications / Issue 8/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2811-9

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