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Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor

1,2,*, 1 and 1
1
Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
2
College of Computer and Information Science, Chongqing Normal University, Chongqing 400050, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(8), 1766; https://doi.org/10.3390/s19081766
Received: 1 March 2019 / Revised: 16 March 2019 / Accepted: 28 March 2019 / Published: 13 April 2019
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Abstract

Automatic detection of left ventricle myocardium is essential to subsequent cardiac image registration and tissue segmentation. However, it is considered challenging mainly because of the complex and varying shape of the myocardium and surrounding tissues across slices and phases. In this study, a hybrid model is proposed to detect myocardium in cardiac magnetic resonance (MR) images combining region proposal and deep feature classification and regression. The model firstly generates candidate regions using new structural similarity-enhanced supervoxel over-segmentation plus hierarchical clustering. Then it adopts a deep stacked sparse autoencoder (SSAE) network to learn the discriminative deep feature to represent the regions. Finally, the features are fed to train a novel nonlinear within-class neighborhood preserved soft margin support vector (C-SVC) classifier and multiple-output support vector ( ε -SVR) regressor for refining the location of myocardium. To improve the stability and generalization, the model also takes hard negative sample mining strategy to fine-tune the SSAE and the classifier. The proposed model with impacts of different components were extensively evaluated and compared to related methods on public cardiac data set. Experimental results verified the effectiveness of proposed integrated components, and demonstrated that it was robust in myocardium localization and outperformed the state-of-the-art methods in terms of typical metrics. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement. View Full-Text
Keywords: myocardium detection; cardiac magnetic resonance; region proposal; support vector classifier and regressor; stacked sparse autoencoder (SSAE) myocardium detection; cardiac magnetic resonance; region proposal; support vector classifier and regressor; stacked sparse autoencoder (SSAE)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Niu, Y.; Qin, L.; Wang, X. Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor. Sensors 2019, 19, 1766.

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