A novel method for image denoising of fluorescence molecular imaging based on fuzzy C-Means clustering

2015 ◽  
Author(s):  
Yu An ◽  
Jie Liu ◽  
Jinzuo Ye ◽  
Yamin Mao ◽  
Xin Yang ◽  
...  
Keyword(s):  
Molecular Imaging ◽  
Image Denoising ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Novel Method ◽  
Fluorescence Molecular Imaging

scholarly journals Optimal Combination of Aircraft Maintenance Tasks by a Novel Simplex Optimization Method

2015 ◽  
Vol 2015 ◽  
pp. 1-19
Author(s):  
Huaiyuan Li ◽  
Hongfu Zuo ◽  
Dan Lei ◽  
Kun Liang ◽  
Tingting Lu
Keyword(s):  
Clustering Algorithm ◽  
Optimization Method ◽  
Optimal Combination ◽  
Maintenance Cost ◽  
The Novel ◽  
Simplex Optimization ◽  
Fuzzy C Means ◽  
Clustering Model ◽  
Fuzzy C Means Clustering ◽  
Novel Method

Combining maintenance tasks into work packages is not only necessary for arranging maintenance activities, but also critical for the reduction of maintenance cost. In order to optimize the combination of maintenance tasks by fuzzy C-means clustering algorithm, an improved fuzzy C-means clustering model is introduced in this paper. In order to reduce the dimension, variables representing clustering centers are eliminated in the improved cluster model. So the improved clustering model can be directly solved by the optimization method. To optimize the clustering model, a novel nonlinear simplex optimization method is also proposed in this paper. The novel method searches along all rays emitting from the center to each vertex, and those search directions are rightlyn+1positive basis. The algorithm has both theoretical convergence and good experimental effect. Taking the optimal combination of some maintenance tasks of a certain aircraft as an instance, the novel simplex optimization method and the clustering model both exhibit excellent performance.

MR image denoising using nonlinear regression and Fuzzy C-Means clustering

2011 ◽  
Author(s):  
Dinh Hoan Trinh ◽  
Marie Luong ◽  
Jean-Marie Rocchisani ◽  
Canh Duong Pham ◽  
Francoise Dibos ◽  
...  
Keyword(s):  
Image Denoising ◽  
Nonlinear Regression ◽  
Mr Image ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Detection of double defects for plate-like structures based on a fuzzy c-means clustering algorithm

2018 ◽  
Vol 18 (3) ◽  
pp. 757-766 ◽  
Author(s):  
Shaojie Chen ◽  
Shaoping Zhou ◽  
Chaofeng Chen ◽  
Yong Li ◽  
Shuangmiao Zhai
Keyword(s):  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Circular Array ◽  
Location Error ◽  
Direct Wave ◽  
Fuzzy C Means ◽  
Single Defect ◽  
Fuzzy C Means Clustering ◽  
Signal Processing Algorithms ◽  
Novel Method

A variety of signal processing algorithms have been proposed to detect and locate defects in plate-like structures. However, the signal-to-noise ratio in these algorithms is too small especially in the reflection wave from the boundary, which further degrades the accuracy of localization of defects. A novel method for localization of defects is proposed in this article, based on the direct wave and fuzzy c-means clustering algorithm. To verify its effectiveness, experiments using the parallel linear and circular array are conducted, respectively. The experimental results show that the proposed method not only accurately locates single defect but also locates double defects in plate-like structures, and by comparing with the current discrete elliptic imaging algorithm, its location error of single defect is reduced from 20–25 mm to 0–3 mm and double defects is also reduced from 60–90 mm to 0–3 mm.

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.

Fluorescence Molecular Imaging of Small Animal Tumor Models

2004 ◽  
Vol 4 (4) ◽  
pp. 419-430 ◽  
Author(s):  
E. Graves ◽  
R. Weissleder ◽  
V. Ntziachristos
Keyword(s):  
Molecular Imaging ◽  
Small Animal ◽  
Tumor Models ◽  
Animal Tumor ◽  
Fluorescence Molecular Imaging
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