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

Authors :
Ju Qiao
Xuezhu Cai
Qian Xiao
Zhengxi Chen
Praveen Kulkarni
Craig Ferris
Sagar Kamarthi
Srinivas Sridhar
Source :
Data in Brief, Vol 27, Iss , Pp - (2019)
Publication Year :
2019
Publisher :
Elsevier, 2019.

Abstract

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. Keywords: Ischemic stroke, Lesion, Magnetic resonance image (MRI), Segmentation

Details

Language :
English
ISSN :
23523409
Volume :
27
Issue :
-
Database :
Directory of Open Access Journals
Journal :
Data in Brief
Publication Type :
Academic Journal
Accession number :
edsdoj.1d837173d4b84a2f916271425d110cb1
Document Type :
article
Full Text :
https://doi.org/10.1016/j.dib.2019.104628