  Bone age assessment is a task performed daily in hospitals worldwide. This
involves a clinician estimating the age of a patient from a radiograph of the
non-dominant hand.
  Our approach to automated bone age assessment is to modularise the algorithm
into the following three stages: segment and verify hand outline; segment and
verify bones; use the bone outlines to construct models of age. In this paper
we address the final question: given outlines of bones, can we learn how to
predict the bone age of the patient? We examine two alternative approaches.
Firstly, we attempt to train classifiers on individual bones to predict the
bone stage categories commonly used in bone ageing. Secondly, we construct
regression models to directly predict patient age.
  We demonstrate that models built on summary features of the bone outline
perform better than those built using the one dimensional representation of the
outline, and also do at least as well as other automated systems. We show that
models constructed on just three bones are as accurate at predicting age as
expert human assessors using the standard technique. We also demonstrate the
utility of the model by quantifying the importance of ethnicity and sex on age
development. Our conclusion is that the feature based system of separating the
image processing from the age modelling is the best approach for automated bone
ageing, since it offers flexibility and transparency and produces accurate
estimates.
