Assessing the Bone Age of Children in an Automatic Manner Newborn to 18 Years Range

Abstract

Bone age assessment (BAA) is a radiological process to identify the growth disorders in children. Although this is a frequent task for radiologists, it is cumbersome. The objective of this study is to assess the bone age of children from newborn to 18 years old in an automatic manner through computer vision methods including histogram of oriented gradients (HOG), local binary pattern (LBP), and scale invariant feature transform (SIFT). Here, 442 left-hand radiographs are applied from the University of Southern California (USC) hand atlas. In this experiment, for the first time, HOG-LBP-dense SIFT features with background subtraction are applied to assess the bone age of the subject group. For this purpose, features are extracted from the carpal and epiphyseal regions of interest (ROIs). The SVM and 5-fold cross-validation are used for classification. The accuracy of female radiographs is 73.88 and of the male is 68.63. The mean absolute error is 0.5 years for both genders' radiographs. The accuracy a within 1-year range is 95.32 for female and 96.51 for male radiographs. The accuracy within a 2-year range is 100 and 99.41 for female and male radiographs, respectively. The Cohen's kappa statistical test reveals that this proposed approach, Cohen's kappa coefficients are 0.71 for female and 0.66 for male radiographs, p value < 0.05, is in substantial agreement with the bone age assessed by experienced radiologists within the USC dataset. This approach is robust and easy to implement, thus, qualified for computer-aided diagnosis (CAD). The reduced processing time and number of ROIs facilitate BAA.