Abstract
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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Aquino A, Gegúndez-Arias ME, Marín D (2010) Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Trans Med Imaging 29:1860–1869
Buntine W (1989) Learning classification rules using Bayes. In: Processing 6th international workshop machine learning, pp 94–96
Cannon R, Dave JV, Bezdek JC (1986) Efficient implementation of the fuzzy C-means clustering algorithms. IEEE Trans Pattern Anal Mach Intell 2(8):248–255
Chang CD, Rong WW (1998) Image contrast enhancement based on a histogram transformation of local standard deviation. IEEE Trans Med Imaging 17:518–531. doi:10.1109/42.730397
Finlaysona G, Steven H, Gerald S, Tian GY (2004) Illuminant and device invariant colour using histogram equalization. Pattern Recognit 38:179–190. doi:10.1016/j.patcog.2004.04.010
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
Gardner GG, Keating D, Williamson TH, Elliott AT (1996) Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol 80(11):940–944
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 413–420
Osareh A, Shadgar B, Markham R (2009) Computational intelligence based approach for detection of exudates in diabetic retinopathy images. IEEE Trans Inf Technol Biomed 13:535–545
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
Sangwine S, Horne R (1998) The colour image processing handbook. Chapman and Hall, London
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
Walter T, Klevin JC, Massin P (2002) A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudate in color fundus images of the human retina. IEEE Trans Med Imaging 21(10):1236–1243
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 181–186
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
Acknowledgements
Funding was provided by Mahasarakham University.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
None.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Wisaeng, K., Sa-ngiamvibool, W. Improved fuzzy C-means clustering in the process of exudates detection using mathematical morphology. Soft Comput 22, 2753–2764 (2018). https://doi.org/10.1007/s00500-017-2532-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-017-2532-8