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

被引：19

作者：

Qiao, Ju ^{[1
]}

Cai, Xuezhu ^{[2
]}

Xiao, Qian ^{[3
]}

Chen, Zhengxi ^{[4
]}

Kulkarni, Praveen ^{[5
]}

Ferris, Craig ^{[5
]}

Kamarthi, Sagar ^{[1
]}

Sridhar, Srinivas ^{[6
]}

机构：

[1] Northeastern Univ, Dept Mech & Ind, Boston, MA 02115 USA

[2] Northeastern Univ, Dept Bioengn, Boston, MA 02115 USA

[3] Yale Univ, Dept Pharmacol, New Haven, CT USA

[4] Shanghai Jiao Tong Univ, Shanghai Peoples Hosp 9, Dept Orthodont, Shanghai, Peoples R China

[5] Northeastern Univ, Dept Psychol, Boston, MA 02115 USA

[6] Northeastern Univ, Dept Phys, Boston, MA 02115 USA

来源：

DATA IN BRIEF | 2019年 / 27卷

关键词：

Ischemic stroke; Lesion; Magnetic resonance image (MRI); Segmentation;

D O I：

10.1016/j.dib.2019.104628

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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. (c) 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

引用

页数：9

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