Fuzzy c-means algorithm for medical image segmentation

M. Christ, R. Parvathi
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引用次数: 34

Abstract

Clustering of data is a method by which large sets of data are grouped into clusters of smaller sets of similar data. Fuzzy c-means (FCM) clustering algorithm is one of the most commonly used unsupervised clustering technique in the field of medical imaging. Medical image segmentation refers to the segmentation of known anatomic structures from medical images. Fuzzy C-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. Fuzzy logic is a multi-valued logic derived from fuzzy set theory. FCM is popularly used for soft segmentations like brain tissue model. And also FCM can provide better results than other clustering algorithms like KM, EM, and KNN. In this paper we presented the medical image segmentation techniques based on various type of FCM algorithms.
医学图像分割的模糊c均值算法
数据聚类是一种方法,通过该方法,大型数据集被分组到较小的类似数据集的聚类中。模糊c均值(FCM)聚类算法是医学成像领域中最常用的无监督聚类技术之一。医学图像分割是指对医学图像中已知的解剖结构进行分割。模糊c均值(FCM)是一种聚类方法,它允许一个数据属于两个或多个聚类。模糊逻辑是由模糊集合理论衍生而来的一种多值逻辑。FCM广泛应用于脑组织模型等软分割。而且FCM可以提供比其他聚类算法(如KM, EM和KNN)更好的结果。本文介绍了基于各种FCM算法的医学图像分割技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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