Robust detection of sources based on clustering in spatially correlated noise fields
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Molecular image segmentation based on improved fuzzy clustering
Journal of Biomedical Imaging
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Pattern Recognition Letters
Robust fuzzy clustering-based image segmentation
Applied Soft Computing
Robust Motion Detection via the Fuzzy Fusion of 6D Feature Space Decompositions
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
An adaptive ant-based clustering algorithm with improved environment perception
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
Segmentation and Edge Detection Based on Spiking Neural Network Model
Neural Processing Letters
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
A color- and texture-based image segmentation algorithm
Machine Graphics & Vision International Journal
A framework with modified fast FCM for brain MR images segmentation
Pattern Recognition
DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric
Applied Soft Computing
CompIMAGE'10 Proceedings of the Second international conference on Computational Modeling of Objects Represented in Images
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An image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper. In the conventional FCM clustering algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and does not take into consideration the spatial distribution of pixels in an image. By introducing a novel dissimilarity index in the modified FCM objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus exploiting the high inter-pixel correlation inherent in most real-world images. The incorporation of local spatial continuity allows the suppression of noise and helps to resolve classification ambiguity. To account for smooth intensity variation within each homogenous region in an image, a multiplicative field is introduced to each of the fixed FCM cluster prototype. The multiplicative field effectively makes the fixed cluster prototype adaptive to slow smooth within-cluster intensity variation, and allows homogenous regions with slow smooth intensity variation to be segmented as a whole. Experimental results with synthetic and real color images have shown the effectiveness of the proposed algorithm.