Bone age assessment using support vector regression with smart class mapping

Daniel Haak; Jing Yu; Hendrik Simon; Hauke Schramm; Thomas Seidl; Thomas M. Deserno

doi:10.1117/12.2008029

28 February 2013 Bone age assessment using support vector regression with smart class mapping

Daniel Haak, Jing Yu, Hendrik Simon, Hauke Schramm, Thomas Seidl, Thomas M. Deserno

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86700A (2013) https://doi.org/10.1117/12.2008029
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Bone age assessment on hand radiographs is a frequently and time consuming task to determine growth disturbances in human body. Recently, an automatic processing pipeline, combining content-based image retrieval and support vector regression (SVR), has been developed. This approach was evaluated based on 1,097 radiographs from the University of Southern California. Discretization of SVR continuous prediction to age classes has been done by (i) truncation. In this paper, we apply novel approaches in mapping of SVR continuous output values: (ii) rounding, where 0.5 is added to the values before truncation; (iii) curve, where a linear mapping curve is applied between the age classes, and (iv) age, where artificial age classes are not used at all. We evaluate these methods on the age range of 0-18 years, and 2-17 years for comparison with the commercial product BoneXpert that is using an active shape approach. Our methods reach root-mean-square (RMS) errors of 0.80, 0.76 and 0.73 years, respectively, which is slightly below the performance of the BoneXpert.

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Daniel Haak, Jing Yu, Hendrik Simon, Hauke Schramm, Thomas Seidl, and Thomas M. Deserno "Bone age assessment using support vector regression with smart class mapping", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700A (28 February 2013); https://doi.org/10.1117/12.2008029

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