Pattern Recognition Letters
Hard C-means clustering for voice activity detection
Speech Communication
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
Pattern Recognition Letters
Novel segmentation algorithm in segmenting medical images
Journal of Systems and Software
Modified fuzzy c-means algorithm for segmentation of T1-T2-weighted brain MRI
Journal of Computational and Applied Mathematics
Local-global neuro-fuzzy system for color change modelling
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
Image segmentation based on FCM with mahalanobis distance
ICICA'10 Proceedings of the First international conference on Information computing and applications
A framework with modified fast FCM for brain MR images segmentation
Pattern Recognition
Short Communication: Image segmentation using PSO and PCM with Mahalanobis distance
Expert Systems with Applications: An International Journal
Modified bias field fuzzy C-means for effective segmentation of brain MRI
Transactions on computational science VIII
Modified bias field fuzzy C-means for effective segmentation of brain MRI
Transactions on computational science VIII
Fuzzy Optimization and Decision Making
Fuzzy c-means clustering with weighted image patch for image segmentation
Applied Soft Computing
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
Monte Carlo cluster refinement for noise robust image segmentation
Journal of Visual Communication and Image Representation
Kernel generalized fuzzy c-means clustering with spatial information for image segmentation
Digital Signal Processing
Image segmentation of noisy digital images using extended fuzzy C-means clustering algorithm
International Journal of Computer Applications in Technology
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Fuzzy c-means (FCM) clustering algorithm has been widely used in many medical image segmentations. However, the conventionally standard FCM algorithm is noise sensitive because of not taking into account the spatial information. To overcome the above problem, a novel modified FCM algorithm (called FCM-AWA later) for image segmentation is presented in this paper. The algorithm is realized by modifying the objective function in the conventional FCM algorithm, i.e., by incorporating the spatial neighborhood information into the standard FCM algorithm. An adaptive weighted averaging (AWA) filter is given to indicate the spatial influence of the neighboring pixels on the central pixel. The parameters (weighting coefficients) of control template (neighboring widow) are automatically determined in the implementation of the weighted averaging image by a predefined nonlinear function. The presented algorithm is applied to both artificial synthesized image and real image. Furthermore, the quantifications of dental plaque using proposed algorithm-based segmentation were conducted. Experimental results show that the presented algorithm performs more robust to noise than the standard FCM algorithm and another FCM algorithm (proposed by Ahmed) do. Furthermore, the results of dental plaque quantification using proposed method indicate the FCM-AWA provides a quantitative, objective and efficient analysis of dental plaque, and possesses great promise.