Bone age assessment (BAA) is a task performed daily by paediatricians in hospitalsworldwide. The main reasons for BAA to be performed are: fi�rstly, diagnosis of growth disorders through monitoring skeletal development; secondly, prediction of final adult height; and fi�nally, verifi�cation of age claims. Manually predicting bone age from radiographs is a di�fficult and time consuming task. This thesis investigates bone age assessment and why automating the process will help. A review of previous automated bone age assessment systems is undertaken and we investigate why none of these systems have gained widespread acceptance. We propose a new automated method for bone age assessment, ASMA (Automated Skeletal Maturity Assessment).
The basic premise of the approach is to automatically extract descriptive shape features that capture the human expertise in forming bone age estimates. The algorithm consists of the following six modularised stages: hand segmentation; hand
segmentation classifi�cation; bone segmentation; feature extraction; bone segmentation classifi�cation; bone age prediction.
We demonstrate that ASMA performs at least as well as other automated systems and that models constructed on just three bones are as accurate at predicting age as expert human assessors using the standard technique. We also investigate the importance of ethnicity and gender in skeletal development. Our conclusion is that the feature based system of separating the image processing from the age modelling is
the best approach, since it off�ers flexibility and transparency, and produces accurate estimates
