Abstract—A traditional approach to segmentation of magnetic resonance (MR) images is the Fuzzy C-Means (FCM) clustering algorithm. However, the conventionally standard FCM algorithm is sensitive to noise. To overcome the above problem, a modified FCM algorithm (called MS-FCM later) for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster. In The proposed algorithm every point of the data set has a weight in relation to every cluster. Therefore this weight permits to have a better classification especially in the case of noise data. The proposed algorithm is applied to both artificial synthesized image and real image. Segmentation results demonstrate that the presented algorithm performs more robust to noise than the standard FCM algorithm.
Index Terms—Fuzzy C-Means, spatial information, image segmentation, membership weighting.
H. Shamsi is with the Department of Electrical and Computer Engineering, University of Ataturk, Turkey (e-mail: Hamed.shamsi@atauni.edu.tr).
H. Seyedarabi is with the Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran (e-mail: Seyedarabi@tabrizu.ac.ir).
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Cite: Hamed Shamsi and Hadi Seyedarabi, "A Modified Fuzzy C-Means Clustering with Spatial Information for Image Segmentation,"
International Journal of Computer Theory and Engineering vol. 4, no. 5, pp. 762-766, 2012.
