Fuzzy c-means clustering with spatial information for image segmentation
Fuzzy c-means clustering with spatial information for image segmentation
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DOI：
10.1016/j.compmedimag.2005.10.001
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发表时间：
2006-01-01
影响因子：
5.7
通讯作者：
Chen, TJ
中科院分区：
文献类型：
--
作者：
Chuang, KS;Tzeng, HL;Chen, TJ
传统的FCM算法没有充分利用图像中的空间信息。在本文中，我们提出了一种模糊 C 均值 (FCM) 算法，该算法将空间信息合并到用于聚类的隶属函数中。空间函数是所考虑的每个像素的邻域中的隶属函数的总和。新方法的优点如下：（1）它产生的区域比其他方法更均匀，（2）它减少了虚假斑点，（3）它消除了噪声点，（4）它比其他技术对噪声不太敏感。该技术是一种强大的噪声图像分割方法，适用于具有空间信息的单特征数据和多特征数据。 (c) 2005 年，爱思唯尔有限公司出版。
A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information. (c) 2005 Published by Elsevier Ltd.