scholarly journals Fuzzy c-Means Clustering, Entropy Maximization, and Deterministic and Simulated Annealing

10.5772/48659 ◽  
2012 ◽  
Author(s):  
Makoto Yasuda
Keyword(s):  
Simulated Annealing ◽  
Entropy Maximization ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 181976-181987
Author(s):  
Jianbin Xiong ◽  
Xi Liu ◽  
Xingtong Zhu ◽  
Hongbin Zhu ◽  
Haiying Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Fault Diagnosis ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

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.

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