Bone age assessment using support vector regression with smart class mapping

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.