Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/9674
Title: Retinal blood vessels extraction using probabilistic modelling
Authors: Kaba, D
Wang, C
Li, Y
Salazar-Gonzalez, A
Liu, X
Serag, A
Keywords: Retinal images;Vessel segmentation;Expectation maximisation
Issue Date: 2014
Publisher: BioMed Central
Citation: Health Information Science and Systems, 2: 2, (27 January 2014)
Abstract: The analysis of retinal blood vessels plays an important role in detecting and treating retinal diseases. In this review, we present an automated method to segment blood vessels of fundus retinal image. The proposed method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. This study combines the bias correction and an adaptive histogram equalisation to enhance the appearance of the blood vessels. Then the blood vessels are extracted using probabilistic modelling that is optimised by the expectation maximisation algorithm. The method is evaluated on fundus retinal images of STARE and DRIVE datasets. The experimental results are compared with some recently published methods of retinal blood vessels segmentation. The experimental results show that our method achieved the best overall performance and it is comparable to the performance of human experts.
Description: © 2014 Kaba et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article has been made available through the Brunel Open Access Publishing Fund.
URI: http://www.hissjournal.com/content/2/1/2
http://bura.brunel.ac.uk/handle/2438/9674
DOI: http://dx.doi.org/10.1186/2047-2501-2-2
ISSN: 2047-2501
Appears in Collections:Brunel OA Publishing Fund
Dept of Computer Science Research Papers

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