A Modified Fuzzy C-Means Algorithm Brain MR Images Segmentation with Bias Field Compensation

Abstract:

Segmentation of brain magnetic resonance (MR) images is always required as a preprocessing stage in many brain analysis tasks. Nevertheless, the bias field (BF, also called intensity in-homogeneities) and noise in the MRI images always make the accurate segmentation difficult. In this paper, we present a modified FCM algorithm for bias field estimation and segmentation of brain MRI. Our method is formulated by modifying the objective function of the standard FCM algorithm. It aims to compensate for bias field and incorporate both the local and non-local information into the distance function to restrain the noise of the image. We have conducted extensive experimental and have compared our method with different types of FCM extension methods using simulated MRI images. The results show that our proposed method can deal with the bias field and noise effectively and outperforms other methods.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 1349-1355
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.1349
• Online since: September 2013
• Authors: Xiao Li Liu, Yu Ting Guo, Jun Kong, Jian Zhong Wang
• Keywords: Bias Field, Fuzzy C-Means, Magnetic Resonance Image (MRI), Non-Local Information

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