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
- Buntine W (1989) Learning classification rules using Bayes. In: Processing 6th international workshop machine learning, pp 94–96Google Scholar
- Garcia M, Hormero R, Sanchez C, Lopez M, Diez A (2007) Feature extraction and selection for automatic detection of hard exudate in retinal images. In: IEEE conference for engineering in medicine and biology society, pp 4969–4972. doi: 10.1109/IEMBS.2007.4353456
- Leung KM (2007) Naive Bayesian classifier (Online). http://cis.poly.edu/~mleung/FRE7851/f07/naiveBayesianClassifier.pdf. Accessed Oct 2016
- Osareh A (2002) Comparative exudate classification using support vector machines and neural networks. In: Proceeding of the 5th international conference on medical image computing and computer-assisted intervention-part II. Springer, London, pp 413–420Google Scholar
- Osareh A, Mirmehdi M, Thomas B, Markham R (2002) Classification and localization of diabetic-related eye disease. In: Proceeding of the 7th European conference on computer vision, pp 502–516. http://dl.acm.org/citation.cfm?id=649256
- Vanrell M, Lumbreras F, Pujol A, Baldrich R, Llados J, Villanueva J J (2001) Color normalization based on background information. In: International conference on image processing, pp 1111–1127. doi: 10.1109/ICIP.2001.959185
- Wang H, Hsu W, Goh HG, Lee ML (2000) An effective approach to detect lesions in color retinal images. In: Proceeding of the IEEE computer society conference on computer vision and pattern recognition, pp 181–186Google Scholar
- Zhang X, Chutatape O (2005) Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, vol 2. IEEE Computer Society, pp 422–428. doi: 10.1109/CVPR.2005.346