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| Detecting and Locating the Macular Using Morphological Features and k-means Clustering |
| Cao Xinrong1,2* , Lin Jiawen1, Xue Lanyan1, Yu Lun1 |
1School of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
2Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou 350121, China |
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Abstract Color fundus images have been widely used in the diagnosis and screening of ophthalmic diseases. The macular detection and foveal location in fundus images are important steps in grading and diagnosis of ophthalmic diseases. An efficient method not relying on the optic and vascular information was proposed in this work for detecting and locating macular foveal. After a general analysis on morphological characteristics of macular, which was low brightness and round, the area of macular could be ensured in the binary images. Then, an improvedk-means clustering method was proposed on the basis of spatial information of images and optimizes clustering objects to obtain the edge information of macular and achieve accurate position of the macula foveal. Experimental tests showed good performance in the public fundus images database. For the normal and the pathological changes of the fundus images, the effective location rate of the macula was 96.11% and 92.12% respectively, and the average accuracy reached 93.92%. Thus the proposed method based on morphological features and k-means clustering proved a simple, efficient and useful tool for computer-aided diagnosis of ocular diseases.
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Received: 26 March 2017
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