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Abstract
In this paper, we present an automated method to segment blood vessels in fundus retinal images. The method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. Our method combines the bias correction to correct the intensity inhomogeneity of the retinal image, and a matched filter to enhance the appearance of the blood vessels. The blood vessels are then extracted from the matched filter response image using the Expectation Maximisation algorithm. The method is tested on fundus retinal images of STARE dataset and 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.
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Authors and Affiliations
Department of Information Systems, Computing and Mathematics, Brunel University, United Kingdom
Djibril Kaba, Ana G. Salazar-Gonzalez, Yongmin Li & Xiaohui Liu
Diagnostic Imaging and Radiology, Children’s National Medical Center, Washington, DC, USA
Ahmed Serag
- Djibril Kaba
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- Ana G. Salazar-Gonzalez
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- Yongmin Li
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- Xiaohui Liu
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- Ahmed Serag
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Editors and Affiliations
Centre for Applied Informatics, Victoria University, 3011, Melbourne, VIC, Australia
Guangyan Huang & Jing He &
School of Information System, Computing, and Mathematics, Brunel University, UB8 3PH, London, UK
Xiaohui Liu
Department of Computer Science, Ostfalia University of Applied Sciences, Salzdahlumer Str. 46/48, Wolfenbüttel, Germany
Frank Klawonn
Health Economics, Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
Guiqing Yao
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Kaba, D., Salazar-Gonzalez, A.G., Li, Y., Liu, X., Serag, A. (2013). Segmentation of Retinal Blood Vessels Using Gaussian Mixture Models and Expectation Maximisation. In: Huang, G., Liu, X., He, J., Klawonn, F., Yao, G. (eds) Health Information Science. HIS 2013. Lecture Notes in Computer Science, vol 7798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37899-7_9
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