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Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Background

This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images.

Methods

T1-weighted (T1W) and T2-weighted (T2W) images of the 15 boys with DMD and 15 normal controls were obtained. Textural features of the images were extracted and wavelet decomposed, and then, principal features were selected. Scale transform was then performed for MRI images. Afterward, SVM-based classifiers of MRI images were analyzed based on the radical basis function and decomposition levels. The cost (C) parameter and kernel parameter \(\gamma \) were used for classification. Then, the optimal SVM-based classifier, expressed as \((C,\gamma \)), was identified by performance evaluation (sensitivity, specificity and accuracy).

Results

Eight of 12 textural features were selected as principal features (eigenvalues \(\lambda _{\mathrm{c}}\ge 1\)). The 16 SVM-based classifiers were obtained using combination of (C, \(\gamma \)), and those with lower C and \(\gamma \) values showed higher performances, especially classifier of \((C = 1,\gamma = 0.083)\,(p<0.05\)). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of \((C = 1,\gamma = 0.083\)) at level 2 decomposition showed the highest performance of all, and its overall correct sensitivity, specificity, and accuracy reached 96.9, 97.3, and 97.1 %, respectively.

Conclusion

The T1W images in SVM-based classifier \((C =1, \gamma = 0.083)\) at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.

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Funding

This study was supported by Key research project of Shanghai municipal government for private universities (2012-SHNGE-01ZD); 2015 joint research project between IBM and universities: clinical medical data analysis and processing (D-2111-15-001).

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Authors and Affiliations

  1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People’s Republic of China

    Ming-Huan Zhang & Jun-Shan Ma

  2. Lab of Big Data Analyses and Process, Information Science and Technology, Shanghai Sanda University, 2727 Jinhai Road, Pudong New District, Shanghai, 201209, People’s Republic of China

    Ming-Huan Zhang & Ying Chen

  3. XinHua Hospital Affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, People’s Republic of China

    Ying Shen

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  1. Ming-Huan Zhang
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Correspondence to Ying Chen.

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Zhang, MH., Ma, JS., Shen, Y. et al. Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines. Int J CARS 11, 1755–1763 (2016). https://doi.org/10.1007/s11548-015-1312-0

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  • DOI: https://doi.org/10.1007/s11548-015-1312-0

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