Statistical shape model-based segmentation of digital X-ray images

Proposes an improved search procedure for Active Shape Model (ASM) based delineation of anatomical structures in digital X-ray images and compare the original ASM search method with this new technique and a search method optimizing a Bayesian objective function, based on the prior knowledge of the statistical variation of the object boundary points and the variation of image-profiles. The original ASM search method iteratively improves the current estimate of the location of boundary points by a limited least squares adjustment of the pose and shape parameters, the authors´ method additionally requires the subsequent changes in shape during the search to be smooth, which is achieved by using a minimum cost path search algorithm, while the Bayesian search methods uses the prior knowledge of the shape-model to constrain the shape parameters within acceptable limits. A number of experiments were performed to compare the accuracy and robustness of all methods using a cross-validation procedure