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A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation
- Source :
-
Signal Processing . Apr2011, Vol. 91 Issue 4, p988-999. 12p. - Publication Year :
- 2011
-
Abstract
- Abstract: Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions (GIFP_FCM) is a novel fuzzy clustering algorithm. However when GIFP_FCM is applied to image segmentation, it is sensitive to noise in the image because of ignoring the spatial information contained in the pixels. In order to solve this problem, a novel fuzzy clustering algorithm with non local adaptive spatial constraint (FCA_NLASC) is proposed in this paper. In the proposed method, a novel non local adaptive spatial constraint term is introduced 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 different role in guiding the noisy image segmentation. Segmentation experiments on synthetic and real images, especially magnetic resonance (MR) images, are performed to assess the performance of an FCA_NLASC 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01651684
- Volume :
- 91
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 57162389
- Full Text :
- https://doi.org/10.1016/j.sigpro.2010.10.001