Bone age determination in young children (newborn to 6 years old) using support vector machines

Abstract

Bone age is assessed through a radiological analysis of the left-hand wrist and is then compared to chronological age. A conflict between these two values indicates an abnormality in the development process of the skeleton. This study, conducted on children aged between 0 and 6 years, proposes a computer-based diagnostic system to eliminate the disadvantages of the methods used in bone age determination. For this purpose, primarily an image processing procedure was applied to the X-ray images of the left-hand wrist of children from different ethnic groups aged between 0 and 6 years. A total of 9 features, corresponding to the carpal bones and distal epiphysis of the radius bone with some physiological attributes of the children, were obtained. Then, by using gain ratio, the best 6 features were used for the classification process. Next, the bone age determination process was performed with the obtained features with the help of the support vector machine (SVM), naive Bayes, k-nearest neighborhood, and C4.5 algorithms. Finally, the features used in the determination process and their effects on the accuracies were examined. The results of the designed system showed that SVM method has a better achievement rate than the other methods at a rate of 72.82%. Additionally, in this study, a new feature corresponding to the distance between the centers of gravity of the carpal bones was used for the classification process, and the analysis of the related feature showed that there was a statistically significant difference at P < 0.05 between this feature and bones in children aged between 0 and 6 years.

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