Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation

Cai, Weiling; Chen, Songcan; Zhang, Daoqiang

doi:10.1016/j.patcog.2006.07.011

Cited by 970 publications

(485 citation statements)

References 27 publications

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“…Therefore, it is necessary to extend the method so that driver genes can be determined by not only somatic alterations but also other different types of molecular changes. Also, we can extend our aim of identifying drivers by some methods such as machine learning methods [47][48][49][50][51].…”

Section: Discussionmentioning

confidence: 99%

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Wei

Zhang

et al. 2017

Complexity

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Integration of multi-omics data of cancer can help people to explore cancers comprehensively. However, with a large volume of different omics and functional data being generated, there is a major challenge to distinguish functional driver genes from a sea of inconsequential passenger genes that accrue stochastically but do not contribute to cancer development. In this paper, we present a gene length-based network method, named DriverFinder, to identify driver genes by integrating somatic mutations, copy number variations, gene-gene interaction network, tumor expression, and normal expression data. To illustrate the performance of DriverFinder, it is applied to four cancer types from The Cancer Genome Atlas including breast cancer, head and neck squamous cell carcinoma, thyroid carcinoma, and kidney renal clear cell carcinoma. Compared with some conventional methods, the results demonstrate that the proposed method is effective. Moreover, it can decrease the influence of gene length in identifying driver genes and identify some rare mutated driver genes.

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Section: Discussionmentioning

confidence: 99%

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Wei

Zhang

et al. 2017

Complexity

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“…At the end of the clustering process, each observation is given a degree of membership to each cluster, where the degree is computed based on specific similarity measure [16]. Let X = {x 1 , x 2 , ..., x N } be the observations that we want to be partitioned into K cluster and m is the clusters overlapping scaler, the algorithm assigns a random degree for each observation with each cluster.…”

Section: Fuzzy C-means (Fcm)mentioning

confidence: 99%

Clustering Hyperspectral Data

Alturki¹,

Bchir²

2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…A classical KI method is employed for comparative analysis. A Markov Random Field Fuzzy c-means clustering algorithm [10] (MRFFCM) being a progressive clustering algorithm is employed. By presenting the numerical results on the three datasets we will show the performance of the proposed method.…”

Section: Datasets Description and Experimental Settingsmentioning

confidence: 99%

An Unsupervised Change Detection in Satellite IMAGES Using MRFFCM Clustering

Lakshmi¹,

Sathyamoorthy²,

Kumar³

2015

IJCATR

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This paper presents a new approach for change detection in synthetic aperture radar images by incorporating Markov random field (MRF) within the framework of FCM. The objective is to partition the difference image which is generated from multitemporal satellite images into changed and unchanged regions. The difference image is generated from log ratio and mean ratio images by image fusion technique. The quality of difference image depends on image fusion technique. In the present work; we have proposed an image fusion method based on stationary wavelet transform. To process the difference image is to discriminate changed regions from unchanged regions using fuzzy clustering algorithms. The analysis of the DI is done using Markov random field (MRF) approach that exploits the interpixel class dependency in the spatial domain to improve the accuracy of the final change-detection areas. The experimental results on real synthetic aperture radar images demonstrate that change detection results obtained by the MRFFCM exhibits less error than previous approaches. The goodness of the proposed fusion algorithm by well-known image fusion measures and the percentage correct classifications are calculated and verified.

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Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation

Cited by 970 publications

References 27 publications

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Clustering Hyperspectral Data

An Unsupervised Change Detection in Satellite IMAGES Using MRFFCM Clustering

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