Improved fuzzy C-means clustering in the process of exudates detection using mathematical morphology
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Exudates are a common complication of diabetic retinopathy and the leading cause of blindness in the developing countries, especially in Thailand. The digital retinal images are usually interpreted visually by an expert ophthalmologist in order to diagnose exudates. However, detecting exudates in a large number of the digital retinal images is mostly manual and very expensive in expert ophthalmologist time and subject to human errors. In this research, we propose a novel retinal image analysis for detecting exudates through image preprocessing methods, i.e., histogram matching, local contrast enhancement, median filter, color space selection, and optic disc localization. Our in-depth retinal analysis indicates that the overall image quality is sensitive to the quality score. In the detection process, the exudates are detected by using naïve Bayesian classifier, support vector machine, and fuzzy C-means clustering method. Afterward, the exudates extracted from fuzzy C-means clustering are used as input to the mathematical morphology to obtain the final exudates detection quality score. Additionally, the optimal parameters of the mathematical morphology will increase the accuracy of the results from merely fuzzy C-means clustering method by 12.05%. The combination of these methods demonstrated an overall pixel-based accuracy of 97.45% including 97.12% sensitivity and 97.89% specificity.
KeywordsExudate detection Diabetic retinopathy Digital retinal image Fuzzy C-means clustering Naïve Bayesian Support vector machine Mathematical morphology
Funding was provided by Mahasarakham University.
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Conflict of interest
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