Matrix analysis
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Edges as Outliers: Anisotropic Smoothing Using Local Image Statistics
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Novel modified fuzzy c-means algorithm with applications
Digital Signal Processing
Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Spatial homogeneity-based fuzzy c-means algorithm for image segmentation
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Image Segmentation Based on Adaptive Cluster Prototype Estimation
IEEE Transactions on Fuzzy Systems
A spatially constrained fuzzy hyper-prototype clustering algorithm
Pattern Recognition
Segmentation of brain MR images using intuitionistic fuzzy clustering algorithm
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Edge multi-scale markov random field model based medical image segmentation in wavelet domain
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Lessons to learn from a mistaken optimization
Pattern Recognition Letters
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Intensity inhomogeneity, noise and partial volume (PV) effect render a challenging task for segmentation of brain magnetic resonance (MR) images. Most of the current MR image segmentation methods focus on only one or two of the effects listed above. In this paper, a framework with modified fast fuzzy c-means for brain MR images segmentation is proposed to take all these effects into account simultaneously and improve the accuracy of image segmentations. Firstly, we propose a new automated method to determine the initial values of the centroids. Secondly, an adaptive method to incorporate the local spatial continuity is proposed to overcome the noise effectively and prevent the edge from blurring. The intensity inhomogeneity is estimated by a linear combination of a set of basis functions. Meanwhile, a regularization term is added to reduce the iteration steps and accelerate the algorithm. The weights of the regularization terms are all automatically computed to avoid the manually tuned parameter. Synthetic and real MR images are used to test the proposed framework. Improved performance of the proposed algorithm is observed where the intensity inhomogeneity, noise and PV effect are commonly encountered. The experimental results show that the proposed method has stronger anti-noise property and higher segmentation precision than other reported FCM-based techniques.