Bone age assessment is a method of assigning a level of biological maturity to a child. It is usually performed either by comparing an x-ray of a child's left hand and wrist with an atlas of known bones, or by analysing specific features of bones such as ratios of width to height, or the degree of overlap with other bones. Both methods of assessment are labour intensive and prone to both inter- and intra-observer variability. This is motivation for developing a computerised method of bone age assessment.

The majority of research and development on computerised bone age assessment has focussed on analysing the bones of the hand. The wrist bones, especially the carpal bones, have received far less attention and have only been analysed in young children in which there is clear separation of the bones. An argument is presented that the evidence for excluding the carpal bones from computerised bone age assessment is weak and that research is required to identify the role of carpal bones in the computerised assessment of bone age for children over eight years of age.

Computerised analysis of the carpal bones in older children is a difficult computer vision problem plagued by radiographic noise, poor image contrast, and especially poor definition of bone contours. Traditional image processing methods such as region growing fail and even the very successful Canny linear edge detector can only find the simplest of bone edges in these images. The field of partial differential equation-based image processing provides some possible solutions to this problem, such as the use of active contour models to impose constraints upon the contour continuity. However, many of these methods require regularisation to achieve unique and stable solutions. An important part of this regularisation is image denoising.

Image denoising was approached through development of a noise model for the Kodak computed radiography system, estimation of noise parameters using a robust estimator of noise per pixel intensity bin, and incorporation of the noise model into a denoising method based on oriented Laplacians. The results for this approach only showed a marginal improvement when using the signal-dependent noise model, although this likely reflects how the noise characteristics were incorporated into the anisotropic diffusion method, rather than the principle of this approach. Even without the signal-dependent noise term the oriented Laplacians denoising of the hand-wrist radiographs was very effective at removing noise and preserving edges.