Regional Logistics System Bi-level Programming with Elastic Demand by Using Fuzzy C-means Clustering

2010 ◽  
Author(s):  
Ming-bao Pang ◽  
Fang Dong ◽  
Xin-ping Zhao
Keyword(s):  
Elastic Demand ◽  
Logistics System ◽  
Regional Logistics ◽  
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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