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Robust Fuzzy C-Means Clustering with Spatial Information for Segmentation of Brain Magnetic Resonance Images

Journal Name:

  • International Journal of Science and Engineering Investigations

Publication Year:

  • 2013

Key Words:

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions (GIFP_FCM) is a fuzzy clustering algorithm. GIFP_FCM has not a satisfactory performance in image segmentation when the image is contaminated by noise because of not taking into account any spatial information contained in the pixels. In order to solve this problem, a novel robust fuzzy c-means algorithm with spatial information (RFCM_SI) is proposed in this paper. In the proposed method, a novel nonlocal adaptive spatial constraint term is used to modify the objective function of GIFP_FCM. The characteristic of this technique is that the adaptive spatial parameter for each pixel is designed to make the non-local spatial information of each pixel playing a positive role in guiding the noisy image segmentation. Segmentation experiments on synthetic and real images, especially brain magnetic resonance (MR) images, are performed to assess the performance of an RFCM_SI in comparison with GIFP_FCM and fuzzy c-means clustering algorithms with local spatial constraint. Experimental results show that the proposed method is robust to noise in the image and more effective than the comparative algorithms.
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  • 12
100-105
  • English

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