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Wang Y.-h.,Institute of Forensic Science Ministry of Justice China Shanghai Key Laboratory of Forensic Medicine NO1347 West Guangfu Road Putuo District Shanghai China | Liu T.-a.,Shanghai University | Wei H.,Institute of Forensic Science Ministry of Justice China Shanghai Key Laboratory of Forensic Medicine NO1347 West Guangfu Road Putuo District Shanghai China | Wan L.,Institute of Forensic Science Ministry of Justice China Shanghai Key Laboratory of Forensic Medicine NO1347 West Guangfu Road Putuo District Shanghai China | And 2 more authors.
Journal of Forensic Sciences | Year: 2015

The aim of this study was to automatically classify epiphyses in the distal radius and ulna using a support vector machine (SVM) and to examine the accuracy of the epiphyseal growth grades generated by the support vector machine. X-ray images of distal radii and ulnae were collected from 140 Chinese teenagers aged between 11.0 and 19.0 years. Epiphyseal growth of the two elements was classified into five grades. Features of each element were extracted using a histogram of oriented gradient (HOG), and models were established using support vector classification (SVC). The prediction results and the validity of the models were evaluated with a cross-validation test and independent test for accuracy (PA). Our findings suggest that this new technique for epiphyseal classification was successful and that an automated technique using an SVM is reliable and feasible, with a relative high accuracy for the models. © 2015 American Academy of Forensic Sciences.

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