Ant Colony Optimization-based method for optic cup segmentation in retinal images |
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| Affiliation: | 1. Institute of Industrial Research, Unit 1 St Andrews Court, University of Portsmouth, Hampshire, PO1 2PR, United Kingdom;2. Seagate Technology, Langstone Road, Havant, Hampshire, PO9 1SA, United Kingdom;1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116,China;2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100090,China;3. School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China;1. National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, China;2. Hunan University of Humanities, Science and Technology, School of Energy and Mechanical-electronic Engineering, Loudi, China |
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| Abstract: | An accurate detection of the cup region in retinal images is necessary to obtain relevant measurements for glaucoma detection. In this work, we present an Ant Colony Optimization-based method for optic cup segmentation in retinal fundus images. The artificial agents will construct their solutions influenced by a heuristic that combines the intensity gradient of the optic disc area and the curvature of the vessels. On their own, the exploration capabilities of the agents are limited; however, by sharing the experience of the entire colony, they are capable of obtaining accurate cup segmentations, even in images with a weak or non-obvious pallor. This method has been tested with the RIM-ONE dataset, yielding an average overlapping error of 24.3% of the cup segmentation and an area under the curve (AUC) of 0.7957 using the cup to disc ratio for glaucoma assessment. |
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| Keywords: | Ant Colony Optimization Optic cup segmentation Retinal images |
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