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Automatic Segmentation of Retinal Nerves by Improved Fuzzy-C-Means Clustering

Z. Faizal Khan, Syed Usama Quadri. Published in Fuzzy Systems

International Journal of Applied Information Systems
Year of Publication: 2015
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Z. Faizal Khan, Syed Usama Quadri
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  1. Faizal Z Khan and Syed Usama Quadri. Article: Automatic Segmentation of Retinal Nerves by Improved Fuzzy-C-Means Clustering. International Journal of Applied Information Systems 9(6):7-10, September 2015. BibTeX

    	author = "Z. Faizal Khan and Syed Usama Quadri",
    	title = "Article: Automatic Segmentation of Retinal Nerves by Improved Fuzzy-C-Means Clustering",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 9,
    	number = 6,
    	pages = "7-10",
    	month = "September",
    	note = "Published by Foundation of Computer Science (FCS), NY, USA"


Computer Aided Detection of medical image has been an improved step in the early diagnosis of diseases present in the body. Developing an efficient algorithm for medical image segmentation has been a demanding area of growing research of interest during the last decades. The initial step in computer aided diagnosis of retinal medical image is generally to segment the nerves present in it. The second step is to analyze each area separately to find the presence of pathologies in it. This paper reports on segmenting of the nerves by separating the retinal images using the combination of Improved Fuzzy-C-Means Clustering along with the Enhanced multidimensional multiscale parser (EMMP) algorithm. The performance of this proposed approach is proved to be better for a threshold value of 120. From the experimental results, it has been observed that the proposed segmentation approach provides better segmentation accuracy of 97.4 % in segmenting Retinal nerves.


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Fuzzy-C-Means Clustering, Enhanced multidimensional multiscale parser (EMMP) algorithm, Segmentation, Retinal image.