Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/9674
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dc.contributor.authorKaba, D-
dc.contributor.authorWang, C-
dc.contributor.authorLi, Y-
dc.contributor.authorSalazar-Gonzalez, A-
dc.contributor.authorLiu, X-
dc.contributor.authorSerag, A-
dc.date.accessioned2015-01-07T11:12:36Z-
dc.date.available2014-01-27-
dc.date.available2015-01-07T11:12:36Z-
dc.date.issued2014-
dc.identifier.citationHealth Information Science and Systems, 2: 2, (27 January 2014)en_US
dc.identifier.issn2047-2501-
dc.identifier.urihttp://www.hissjournal.com/content/2/1/2-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/9674-
dc.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.en_US
dc.descriptionThis article has been made available through the Brunel Open Access Publishing Fund.-
dc.description.abstractThe 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.en_US
dc.description.sponsorshipThe Department of Information Systems, Computing and Mathematics, Brunel University.en_US
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.subjectRetinal imagesen_US
dc.subjectVessel segmentationen_US
dc.subjectExpectation maximisationen_US
dc.titleRetinal blood vessels extraction using probabilistic modellingen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1186/2047-2501-2-2-
dc.relation.isPartOfHealth Information Science and Systems-
dc.relation.isPartOfHealth Information Science and Systems-
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pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Computer Science/Computer Science-
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pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups-
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Dept of Computer Science Research Papers

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