scholarly journals Enhanced Image Segmentation: Merging Fuzzy K-Means and Fuzzy C-Means Clustering Algorithms for Medical Applications

2021 ◽  
Vol 9 (1) ◽  
pp. 1-13
Author(s):  
Karim Mohammed Aljebory ◽  
Thabit Sultan Mohammed ◽  
Mohammed U. Zainal
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithms ◽  
Medical Applications ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Robust RML Estimator - Fuzzy C-Means Clustering Algorithms for Noisy Image Segmentation

2011 ◽  
pp. 474-486 ◽  
Author(s):  
Dante Mújica-Vargas ◽  
Francisco Javier Gallegos-Funes ◽  
Alberto J. Rosales-Silva ◽  
Rene Cruz-Santiago
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithms ◽  
Noisy Image ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Yao Yang ◽  
Chengmao Wu ◽  
Yawen Li ◽  
Shaoyu Zhang
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithms ◽  
Feature Space ◽  
Iterative Solution ◽  
Segmentation Algorithm ◽  
Gaussian Kernel ◽  
Fuzzy C Means ◽  
Different Types ◽  
Fuzzy C Means Clustering

To improve the effectiveness and robustness of the existing semisupervised fuzzy clustering for segmenting image corrupted by noise, a kernel space semisupervised fuzzy C-means clustering segmentation algorithm combining utilizing neighborhood spatial gray information with fuzzy membership information is proposed in this paper. The mean intensity information of neighborhood window is embedded into the objective function of the existing semisupervised fuzzy C-means clustering, and the Lagrange multiplier method is used to obtain its iterative expression corresponding to the iterative solution of the optimization problem. Meanwhile, the local Gaussian kernel function is used to map the pixel samples from the Euclidean space to the high-dimensional feature space so that the cluster adaptability to different types of image segmentation is enhanced. Experiment results performed on different types of noisy images indicate that the proposed segmentation algorithm can achieve better segmentation performance than the existing typical robust fuzzy clustering algorithms and significantly enhance the antinoise performance.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

Efficient Implementation of the Fuzzy c-Means Clustering Algorithms

1986 ◽  
Vol PAMI-8 (2) ◽  
pp. 248-255 ◽  
Author(s):  
Robert L. Cannon ◽  
Jitendra V. Dave ◽  
James C. Bezdek
Keyword(s):  
Clustering Algorithms ◽  
Efficient Implementation ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chunhua Ren ◽  
Linfu Sun
Keyword(s):  
Clustering Algorithms ◽  
Real Data ◽  
Local Information ◽  
Data Sets ◽  
Clustering Ensemble ◽  
K Nearest Neighbors ◽  
Fuzzy C Means ◽  
Clustering Quality ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

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