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International Journal of Machine Learning and Cybernetics

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22-05-2017 | Original Article

Segmentation of the left ventricle in cardiac MRI using a hierarchical extreme learning machine model

Authors: Yang Luo, Benqiang Yang, Lisheng Xu, Liling Hao, Jun Liu, Yang Yao, Frans van de Vosse

Published in: International Journal of Machine Learning and Cybernetics | Issue 10/2018

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Abstract

Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) images is an essential step for calculation of clinical indices such as stroke volume, ejection fraction. In this paper, a new automatic LV segmentation method combines a Hierarchical Extreme Learning Machine (H-ELM) and a new location method is developed. An H-ELM can achieve more compact and meaningful feature representations and learn the segmentation task from the ground truth. A new automatic LV location method is integrated to improve the accuracy of classification and reduce the cost of segmentation. Experimental results (including 30 cases, 10 cases for training, 20 cases for testing) show that the mean absolute deviation of images segmented by our proposed method is about 67.9, 81.3 and 98.7% of those images segmented by the level set, the SVM and Hu’s method, respectively. The mean maximum absolute deviation of images segmented by our proposed method is about 63.5, 77.3 and 98.0% of those images segmented by the level set, the SVM and Hu’s method, respectively. The mean dice similarity coefficient of images segmented by our proposed method is about 13.7, 9.3 and 0.5% higher than that of those images segmented by the level set, the SVM and Hu’s method, respectively. The mean speed of our proposed method is about 38.3, 6.7 and 23.8 times faster than that of the level set, the SVM and Hu’s method, respectively. The standard deviation of our proposed method is the lowest among four methods. The results validate that our proposed method is efficient and satisfactory for the LV segmentation.

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Title: Segmentation of the left ventricle in cardiac MRI using a hierarchical extreme learning machine model
Authors: Yang Luo
Benqiang Yang
Lisheng Xu
Liling Hao
Jun Liu
Yang Yao
Frans van de Vosse
Publication date: 22-05-2017
Publisher: Springer Berlin Heidelberg
Published in: International Journal of Machine Learning and Cybernetics / Issue 10/2018
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-017-0678-4

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