• Ming-Huan Zhang, Jun-Shan Ma, Ying Shen, and Ying Chen.
• School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.
• Int J Comput Assist Radiol Surg. 2016 Sep 1; 11 (9): 1755-63.

BackgroundThis study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images.MethodsT1-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 [Formula: see text] were used for classification. Then, the optimal SVM-based classifier, expressed as [Formula: see text]), was identified by performance evaluation (sensitivity, specificity and accuracy).ResultsEight of 12 textural features were selected as principal features (eigenvalues [Formula: see text]). The 16 SVM-based classifiers were obtained using combination of (C, [Formula: see text]), and those with lower C and [Formula: see text] values showed higher performances, especially classifier of [Formula: see text]). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of [Formula: see text]) 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.ConclusionThe T1W images in SVM-based classifier [Formula: see text] at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.

12 keywords...

hide…