Suppressed fuzzy c-means clustering algorithm
Pattern Recognition Letters
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
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International Journal of Computer Vision
Digital Image Processing (3rd Edition)
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Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation
Pattern Recognition Letters
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International Journal of Computer Vision
An enhanced possibilistic C-Means clustering algorithm EPCM
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Soft Computing in Decision Modeling; Guest Editors: Vicenc Torra, Yasuo Narukawa
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Neurocomputing
Fuzzy c-means clustering with non local spatial information for noisy image segmentation
Frontiers of Computer Science in China
Suppressed fuzzy-soft learning vector quantization for MRI segmentation
Artificial Intelligence in Medicine
Spatial color image segmentation based on finite non-Gaussian mixture models
Expert Systems with Applications: An International Journal
Image segmentation by histogram thresholding using fuzzy sets
IEEE Transactions on Image Processing
FUAT - A fuzzy clustering analysis tool
Expert Systems with Applications: An International Journal
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Semi-supervised clustering for MR brain image segmentation
Expert Systems with Applications: An International Journal
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Suppressed fuzzy c-means clustering algorithm (S-FCM) is one of the most effective fuzzy clustering algorithms. Even if S-FCM has some advantages, some problems exist. First, it is unreasonable to compulsively modify the membership degree values for all the data points in each iteration step of S-FCM. Furthermore, duo to only utilizing the spatial information derived from the pixel's neighborhood window to guide the process of image segmentation, S-FCM cannot obtain satisfactory segmentation results on images heavily corrupted by noise. This paper proposes an optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation to solve the above drawbacks of S-FCM. Firstly, an optimal-selection-based suppressed strategy is presented to modify the membership degree values for data points. In detail, during each iteration step, all the data points are ranked based on their biggest membership degree values, and then the membership degree values of the top r ranked data points are modified while the membership degree values of the other data points are not changed. In this paper, the parameter r is determined by the golden section method. Secondly, a novel gray level histogram is constructed by using the self-tuning non local spatial information for each pixel, and then fuzzy c-means clustering algorithm with the optimal-selection-based suppressed strategy is executed on this histogram. The self-tuning non local spatial information of a pixel is derived from the pixels with a similar neighborhood configuration to the given pixel and can preserve more information of the image than the spatial information derived from the pixel's neighborhood window. This method is applied to Berkeley and other real images heavily contaminated by noise. The image segmentation experiments demonstrate the superiority of the proposed method over other fuzzy algorithms.