Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization

The data in this article provide details about MRI lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization (GMM-EM) algorithms. Both K-means and GMM-EM algorithms can segment lesion area from the rest of brain MRI automatically. The performance metrics (accuracy, sensit...

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Detalles Bibliográficos
Autores principales: Qiao, Ju, Cai, Xuezhu, Xiao, Qian, Chen, Zhengxi, Kulkarni, Praveen, Ferris, Craig, Kamarthi, Sagar, Sridhar, Srinivas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Medicine and Dentistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6820303/
https://www.ncbi.nlm.nih.gov/pubmed/31687441
http://dx.doi.org/10.1016/j.dib.2019.104628
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author Qiao, Ju
Cai, Xuezhu
Xiao, Qian
Chen, Zhengxi
Kulkarni, Praveen
Ferris, Craig
Kamarthi, Sagar
Sridhar, Srinivas
author_facet Qiao, Ju
Cai, Xuezhu
Xiao, Qian
Chen, Zhengxi
Kulkarni, Praveen
Ferris, Craig
Kamarthi, Sagar
Sridhar, Srinivas
author_sort Qiao, Ju
collection PubMed
description The data in this article provide details about MRI lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization (GMM-EM) algorithms. Both K-means and GMM-EM algorithms can segment lesion area from the rest of brain MRI automatically. The performance metrics (accuracy, sensitivity, specificity, false positive rate, misclassification rate) were estimated for the algorithms and there was no significant difference between K-means and GMM-EM. In addition, lesion size does not affect the accuracy and sensitivity for either method.
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spelling pubmed-68203032019-11-04 Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization Qiao, Ju Cai, Xuezhu Xiao, Qian Chen, Zhengxi Kulkarni, Praveen Ferris, Craig Kamarthi, Sagar Sridhar, Srinivas Data Brief Medicine and Dentistry The data in this article provide details about MRI lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization (GMM-EM) algorithms. Both K-means and GMM-EM algorithms can segment lesion area from the rest of brain MRI automatically. The performance metrics (accuracy, sensitivity, specificity, false positive rate, misclassification rate) were estimated for the algorithms and there was no significant difference between K-means and GMM-EM. In addition, lesion size does not affect the accuracy and sensitivity for either method. Elsevier 2019-10-10 /pmc/articles/PMC6820303/ /pubmed/31687441 http://dx.doi.org/10.1016/j.dib.2019.104628 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Medicine and Dentistry
Qiao, Ju
Cai, Xuezhu
Xiao, Qian
Chen, Zhengxi
Kulkarni, Praveen
Ferris, Craig
Kamarthi, Sagar
Sridhar, Srinivas
Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization
title Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization
title_full Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization
title_fullStr Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization
title_full_unstemmed Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization
title_short Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization
title_sort data on mri brain lesion segmentation using k-means and gaussian mixture model-expectation maximization
topic Medicine and Dentistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6820303/
https://www.ncbi.nlm.nih.gov/pubmed/31687441
http://dx.doi.org/10.1016/j.dib.2019.104628
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