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Abstract: Corneal arcus is a white ring shape variation formed on the edge of the cornea, which is mainly caused by the abnormal lipid metabolism in human body. This corneal abnormality is significantly associated with the lipid disorder and atherosclerosis. Using image analysis method to detect corneal arcus can help people find the abnormal lipid metabolism in human body conveniently and in time. In the image acquired in natural eye open state, the corneal arcus is often occluded by the eyelid randomly, and disturbed by light spot and blood vessel. In order to improve the robustness of the algorithm and reduce the locating error caused by the random occlusion of the eyelid a corneal arcus segmentation method based on multiscale color replacement is proposed. Firstly, the image is quantized. Secondly, the image is processed with the defined color replacement strategy under different scale templates. Finally, the object segmentation is achieved. 1 968 images in our database were used to conduct experiment. Experiment results indicate that with the proposed method the segmentation accuracy for the 1 968 images reaches to 97.0%. The proposed method has high robustness and is not sensitive to noise.
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