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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

01.09.2016 | Original Article

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

verfasst von: Ming-Huan Zhang, Jun-Shan Ma, Ying Shen, Ying Chen

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 9/2016

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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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Titel: Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines
verfasst von: Ming-Huan Zhang
Jun-Shan Ma
Ying Shen
Ying Chen
Publikationsdatum: 01.09.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 9/2016
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-015-1312-0

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

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