doi: 10.7763/IJCTE.2012.V4.573
A Modified Fuzzy C-Means Clustering with Spatial Information for Image Segmentation
Hamed Shamsi1
,
Hadi Seyedarabi2
- 1Department of Electrical and Computer Engineering, University of Ataturk, Turkey.
- 2Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
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.
Keywords
- Fuzzy C-Means
- spatial information
- image segmentation
- membership weighting
How to 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. https://doi.org/10.7763/IJCTE.2012.V4.573
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).