Fast hybrid rough-set theoretic fuzzy clustering technique with application to multispectral image segmentation

2010 ◽  
Author(s):  
Arpit Srivastava ◽  
Abhinav Asati ◽  
Mahua Bhattacharya
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Fuzzy Clustering ◽  
Multispectral Image ◽  
Clustering Technique

Multispectral image segmentation by fuzzy clustering algorithm used Gaussian mixture model

2017 ◽  
Vol 25 (2) ◽  
pp. 509-518 ◽  
Author(s):  
李 玉 LI Yu ◽  
徐 艳 XU Yan ◽  
赵雪梅 ZHAO Xue-mei ◽  
赵泉华 ZHAO Quan-hua
Keyword(s):  
Image Segmentation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Gaussian Mixture ◽  
Multispectral Image ◽  
Fuzzy Clustering Algorithm

Entropy Weighting Based Centralized Multi-View Fuzzy Clustering: A Case Study on Brain MR Image Segmentation

2020 ◽  
Vol 10 (7) ◽  
pp. 1654-1659
Author(s):  
Hengfei Wu ◽  
Guanglei Sheng ◽  
Lin Li
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Analysis ◽  
Medical Image Segmentation ◽  
Mr Image ◽  
Clustering Technique ◽  
Fuzzy Clustering Analysis ◽  
Mr Image Segmentation ◽  
Weight Learning

Multi-view fuzzy clustering analysis is often used for medical image segmentation such as brain MR image segmentation. However, in traditional multi-view clustering, it assumes that each view plays the same role to the final partition result, which omits the negative influences caused by noisy or weak views. In this paper, a novel entropy weighting based centralized clustering technique is proposed for multi-view datasets where the Shannon entropy is hired for view weight learning. Moreover, the centralized strategy is employed for collaborate learning. Extensive experiments show that the promising performance of our proposed clustering technique. More importantly, a case study on brain MR image segmentation indicates the application ability of our clustering technique.

scholarly journals Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

2019 ◽  
Vol 11 (23) ◽  
pp. 2772 ◽  
Author(s):  
Yan Xu ◽  
Ruizhi Chen ◽  
Yu Li ◽  
Peng Zhang ◽  
Jie Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Fuzzy Clustering ◽  
Gaussian Mixture ◽  
Multispectral Image ◽  
Tsallis Entropy ◽  
Segmentation Algorithm ◽  
Multispectral Images ◽  
Fuzzy C Means

Accurate multispectral image segmentation is essential in remote sensing research. Traditional fuzzy clustering algorithms used to segment multispectral images have several disadvantages, including: (1) they usually only consider the pixels’ grayscale information and ignore the interaction between pixels; and, (2) they are sensitive to noise and outliers. To overcome these constraints, this study proposes a multispectral image segmentation algorithm based on fuzzy clustering combined with the Tsallis entropy and Gaussian mixture model. The algorithm uses the fuzzy Tsallis entropy as regularization item for fuzzy C-means (FCM) and improves dissimilarity measure using the negative logarithm of the Gaussian Mixture Model (GMM). The Hidden Markov Random Field (HMRF) is introduced to define prior probability of neighborhood relationship, which is used as weights of the Gaussian components. The Lagrange multiplier method is used to solve the segmentation model. To evaluate the proposed segmentation algorithm, simulated and real multispectral images were segmented using the proposed algorithm and two other algorithms for comparison (i.e., Tsallis Fuzzy C-means (TFCM), Kullback–Leibler Gaussian Fuzzy C-means (KLG-FCM)). The study found that the modified algorithm can accelerate the convergence speed, reduce the effect of noise and outliers, and accurately segment simulated images with small gray level differences with an overall accuracy of more than 98.2%. Therefore, the algorithm can be used as a feasible and effective alternative in multispectral image segmentation, particularly for those with small color differences.

A fully automated hybrid methodology using Cuckoo-based fuzzy clustering technique for magnetic resonance brain image segmentation

2017 ◽  
Vol 27 (4) ◽  
pp. 317-332 ◽  
Author(s):  
Saravanan Alagarsamy ◽  
Kartheeban Kamatchi ◽  
Vishnuvarthanan Govindaraj ◽  
Arunprasath Thiyagarajan
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Fuzzy Clustering ◽  
Brain Image ◽  
Clustering Technique ◽  
Hybrid Methodology ◽  
Brain Image Segmentation

scholarly journals A Novel Histogram-Based Fuzzy Clustering Method for Multispectral Image Segmentation

2017 ◽  
Vol 2 (2) ◽  
pp. 116-121 ◽  
Author(s):  
Guoying Liu ◽  
◽  
Hongyu Zhou ◽  
Jing Lv ◽  
◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Multispectral Image ◽  
Clustering Method ◽  
Fuzzy Clustering Method
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