Automated bone age assessment using bag of features and random forests

Abstract

The Bone Age is a fairly reliable measure of persons growth and maturation of skeleton. Bone age assessment (BAA) is a procedure that is used to predict the age of a person. The construction of a complete and fairly accurate automated bone age assessment system (ABAA) requires efficient feature extraction and classification methods. In this paper, we have presented an implementation of Bag of Features (BoF) method along with Random Forest classifier on phalanges or bones of fingers. The results have outperformed previous methods available as we have achieved a mean error of 0.58 years and 0.77 years of RMSE for bone age range of 0-18 years. Our experiments have also proved that use of gender bias improves the classification. The best performance was obtained for the ring, middle and index fingers. � 2017 IEEE.