Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Pattern Recognition Letters
Regularized fuzzy c-means method for brain tissue clustering
Pattern Recognition Letters
A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction
Pattern Recognition Letters
A new segmentation system for brain MR images based on fuzzy techniques
Applied Soft Computing
Novel modified fuzzy c-means algorithm with applications
Digital Signal Processing
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
A kernelized fuzzy c-means algorithm for automatic magnetic resonance image segmentation
Journal of Computational Methods in Sciences and Engineering
Effective fuzzy c-means based kernel function in segmenting medical images
Computers in Biology and Medicine
Effective fuzzy clustering techniques for segmentation of breast MRI
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Digital Information Forensics
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
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In recent day, segmentation of brain Magnetic resonance Image (MRI) with bias field correction is challenging and unavoidable in high magnetic imaging. The brain MRI is affected by bias field that causes the undesired effect of quantitative image analysis. The removal of bias field distortion is useful in segmenting medical images for proper study of medical images. In this paper, we propose three new Fuzzy c-Means (FCM) algorithms namely Robust Gaussian based Weighted Bias Field FCM [RGBFCM], Spatial constraint Gaussian based bias field FCM [GBFCM_S], Novel Penalized Gaussian based Bias field FCM [NPGBFCM] in order to remove bias field distortion and to obtain well segmentation result. The proposed methods are capable to deal with the intensity in-homogeneities and noisy image effectively. Further, to reduce the number of iterations, the proposed algorithms initialize the centroid using dist-max initialization algorithm before the execution of algorithms iteratively. To show the performance of proposed methods, this paper applies them to segmentation of brain MRIs and compares the results of our proposed methods with other reported methods. The segmentation accuracy of proposed method is validated by using Silhouette method. The experimental results on real T1-T2 weighted and simulated brain MRIs show that our methods are superior in providing better segmentation results than standard fuzzy c-means based algorithms.