Level Set Segmentation of Optic Discs from Retinal Images
Chuang Wang, Djibril Kaba, and Yongmin Li
Department of Computer Science, Brunel University, London, UK
Abstract—Analysis of retinal images can provide important information for detecting and tracing retinal and vascular diseases. The purpose of this work is to design a method that can automatically segment the optic disc in the digital fundus images. The template matching method is used to approximately locate the optic disc centre, and the blood vessel is extracted to reset the centre. This is followed by applying the Level Set Method, which incorporates edge term, distance-regularization term and shape-prior term, to segment the shape of the optic disc. Seven measures are used to evaluate the performance of the methods. The effectiveness of the proposed method is evaluated against alternative methods on three public data sets DRIVE, DIARETDB1 and DIARETDB0. The results show that our method outperforms the state-of-the-art methods on these datasets.
Index Terms—active contours, optic disc segmentation, retinal image, level sets, template matching
Cite: Chuang Wang, Djibril Kaba, and Yongmin Li, "Level Set Segmentation of Optic Discs from Retinal Images," Journal of Medical and Bioengineering, Vol. 4, No. 3, pp. 213-220, June 2015. Doi: 10.12720/jomb.4.3.213-220
Index Terms—active contours, optic disc segmentation, retinal image, level sets, template matching
Cite: Chuang Wang, Djibril Kaba, and Yongmin Li, "Level Set Segmentation of Optic Discs from Retinal Images," Journal of Medical and Bioengineering, Vol. 4, No. 3, pp. 213-220, June 2015. Doi: 10.12720/jomb.4.3.213-220
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