title: Retinal blood vessels extraction using probabilistic modelling.
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.
authors.affiliation: Department of Information Systems, Computing and Mathematics Brunel University, London, UK.
authors.forename: Djibril
authors.initials: D
authors.lastname: Kaba
authors.affiliation: Department of Information Systems, Computing and Mathematics Brunel University, London, UK.
authors.forename: Chuang
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Information Systems, Computing and Mathematics Brunel University, London, UK.
authors.forename: Yongmin
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Information Systems, Computing and Mathematics Brunel University, London, UK.
authors.forename: Ana
authors.initials: A
authors.lastname: Salazar-Gonzalez
authors.affiliation: Department of Information Systems, Computing and Mathematics Brunel University, London, UK.
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Liu
authors.affiliation: Diagnostic Imaging and Radiology, Children's National Medical Center, 24105 Washington, DC USA.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Serag
keywords: Expectation maximisation; Retinal images; Vessel segmentation
references.citation: Comput Methods Programs Biomed. 2009 Nov;96(2):85-95
references.pmid: 19419790
references.citation: IEEE Trans Med Imaging. 2000 Mar;19(3):203-10
references.pmid: 10875704
references.citation: IEEE Trans Med Imaging. 2011 Jan;30(1):146-58
references.pmid: 20699207
references.citation: Biomed Eng Online. 2010 Feb 28;9:14
references.pmid: 20187975
references.citation: IEEE Trans Med Imaging. 2010 Jun;29(6):1310-20
references.pmid: 20378467
references.citation: IEEE Trans Med Imaging. 2004 Apr;23(4):501-9
references.pmid: 15084075
references.citation: Comput Biol Med. 2010 Apr;40(4):438-45
references.pmid: 20202631
references.citation: IEEE Trans Med Imaging. 1998 Feb;17(1):87-97
references.pmid: 9617910
references.citation: IEEE Trans Med Imaging. 1989;8(3):263-9
references.pmid: 18230524
references.citation: IEEE Trans Image Process. 2001;10(7):1010-9
references.pmid: 18249674
references.citation: IEEE Trans Med Imaging. 2006 Sep;25(9):1200-13
references.pmid: 16967805
references.citation: Comput Biol Med. 2007 Feb;37(2):262-7
references.pmid: 16697363
references.citation: Med Image Anal. 2007 Feb;11(1):47-61
references.pmid: 17204445
references.citation: Biometrics. 2005 Jun;61(2):456-64
references.pmid: 16011692