Local convergence analysis of a grouped variable version of coordinate descent
Journal of Optimization Theory and Applications
Pattern Recognition Letters
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Suppressed fuzzy c-means clustering algorithm
Pattern Recognition Letters
Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation
Pattern Recognition Letters
A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network
Expert Systems with Applications: An International Journal
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
A segmentation method for images compressed by fuzzy transforms
Fuzzy Sets and Systems
DS '09 Proceedings of the 12th International Conference on Discovery Science
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Soft Computing in Decision Modeling; Guest Editors: Vicenc Torra, Yasuo Narukawa
A robust fuzzy local information C-means clustering algorithm
IEEE Transactions on Image Processing
Locality sensitive C-means clustering algorithms
Neurocomputing
Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation
Computers in Biology and Medicine
A generalized approach to the suppressed fuzzy c-means algorithm
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
A framework with modified fast FCM for brain MR images segmentation
Pattern Recognition
A non-local fuzzy segmentation method: Application to brain MRI
Pattern Recognition
A spatially constrained fuzzy hyper-prototype clustering algorithm
Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The fuzzy local information c-means (FLICM) algorithm, introduced by Krinidis and Chatzis (2010), was designed to perform highly accurate segmentation of images contaminated with high-frequency noise. This algorithm includes an extra additive term to the objective function of the fuzzy c-means (FCM), called local descriptor fuzzy factor, allowing the labeling of a pixel to be influenced by its neighbors, thus achieving a filtering effect. Further on, the authors of FLICM claim that their algorithm does not depend on any trade-off parameter, which were present in all previous similar approaches. In this paper we investigate the theoretical foundation of FLICM and reveal some critical issues. First of all, we show that the iterative optimization algorithm proposed for the minimization of the FLICM objective function is not suitable for the given problem, it does not minimize the objective function. Instead of that, FLICM computes an FCM-like partition using distorted distances, according to the local context of each pixel, thus performing a job that is similar to the so-called suppressed fuzzy c-means algorithm existing in the literature. Finally we reveal the presence of a possible trade-off in the definition of the local descriptor fuzzy term, and the necessity of another factor to compensate against the size of the considered neighborhood. Such algorithms can be effective in certain scenarios, which were documented by the authors, but a deep investigation of the limitations would be beneficial.