Bone Age Assessment of Iranian Children in an Automatic Manner

Farzaneh Dehghani, Alireza Karimian, Mehri Sirous, Javad Rasti, Ali Soleymanpour

DOI: 10.4103/jmss.JMSS_9_20


Background: Bone age assessment (BAA) is a radiological process with the aim of identifying growth disorders in children. The objective of this study is to assess the bone age of Iranian children in an automatic manner. Methods: In this context, three computer vision techniques including histogram of oriented gradients (HOG), local binary pattern (LBP), and scale-invariant feature transform (SIFT) are applied to extract appropriate features from the carpal and epiphyseal regions of interest. Two different datasets are applied here: the University of Southern California hand atlas for training this computer-aided diagnosis (CAD) system and Iranian radiographs for evaluating the performance of this system for BAA of Iranian children. In this study, the concatenation of HOG, LBP, and dense SIFT feature vectors and background subtraction are applied to improve the performance of this approach. Support vector machine (SVM) and K-nearest neighbor are used here for classification and the better results yielded by SVM. Results: The accuracy of female radiographs is 90% and of male is 71.42%. The mean absolute error is 0.16 and 0.42 years for female and male test radiographs, respectively. Cohen’s kappa coefficients are 0.86 and 0.6, P < 0.05, for female and male radiographs, respectively. The results indicate that this proposed approach is in substantial agreement with the bone age reported by the experienced radiologist. Conclusion: This approach is easy to implement and reliable, thus qualified for CAD and automatic BAA of Iranian children.


Bone age assessment, computer vision operators, Iranian race, K-nearest neighbors, left-hand radiographic images, support vector machine


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