Automatic annotation of Radiographs using Parts and Geometry models for building Statistical Models for skeletal maturity

Statistical Models of Shape and Appearance require annotation of the bones of the hand of children and young adults. Due to very large variation in the shape and appearance of these bones, automatic annotation is particularly challenging. Statistical Models of Shape and Appearance have been found useful in several medical image analysis and other applications. In this work we build a semi-automatic Parts and Geometry model to locate sparse points in each of the Radiographic image. These sparse points were then used as control points to propagate manually annotated points to other images. The resulting propagation may be used to build Statistical models that have be found to be useful in estimating skeletal maturity. By analysing performance on dataset of 537 digitized images of normal children we achieved an automatic annotation accuracy of a mean point to curve error of 1mm ± 0.18 and a median error 0.94mm.