文章基本信息

标题：Gaussian Kernel Based Fuzzy C-Means Clustering Algorithm for Image Segmentation
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作者：Rehna Kalam ; Dr Ciza Thomas ; Dr M Abdul Rahiman 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2016
卷号：6
期号：4
页码：47-56
DOI：10.5121/csit.2016.60405
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Image processing is an important research area in computer vision. clustering is an unsupervisedstudy. clustering can also be used for image segmentation. there exist so many methods for imagesegmentation. image segmentation plays an important role in image analysis.it is one of the firstand the most important tasks in image analysis and computer vision. this proposed systempresents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzyc-means clustering algorithm (kfcm) is derived from the fuzzy c-means clusteringalgorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracysignificantly compared with classical fuzzy c-means algorithm. the new algorithm is calledgaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic ofgkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness andimage detail preservation.. the objective of the work is to cluster the low intensity in homogeneityarea from the noisy images, using the clustering method, segmenting that portion separately usingcontent level set approach. the purpose of designing this system is to produce better segmentationresults for images corrupted by noise, so that it can be useful in various fields like medical imageanalysis, such as tumor detection, study of anatomical structure, and treatment planning.
关键词：CLUSTERING; K-MEANS; FCM; KFCM; GKFCM