A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation

• Published in: Signal processing Vol. 91; no. 4; pp. 988 - 999
• Main Authors: Zhao, Feng, Jiao, Licheng, Liu, Hanqiang, Gao, Xinbo
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2011; Elsevier
• Subjects: Adaptive algorithms; Adaptive spatial parameter; Algorithms; Applied sciences; Biological and medical sciences; Clustering; Computerized, statistical medical data processing and models in biomedicine; Exact sciences and technology; Fuzzy; Fuzzy clustering algorithm; Fuzzy logic; Fuzzy set theory; Image processing; Image segmentation; Information, signal and communications theory; Magnetic resonance (MR) image; Medical management aid. Diagnosis aid; Medical sciences; Non local spatial constraint; Pixels; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory; Non local spatial constraint; Image segmentation; Adaptive spatial parameter; Magnetic resonance (MR) image; Fuzzy clustering algorithm; Performance evaluation; Automatic classification; Image processing; Signal classification; Nuclear magnetic resonance imaging; Noisy image; Fuzzy algorithm; Medical imagery; Objective function
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2010.10.001

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.