Evaluation of image features and search strategies for segmentation of bone structures in radiographs using Active Shape Models.

In this paper, we evaluate various image features and different search strategies for fitting Active Shape Models (ASM) to bone object boundaries in digitized radiographs. The original ASM method iteratively refines the pose and shape parameters of the point distribution model driving the ASM by a least squares fit of the shape to update the target points at the estimated object boundary position, as determined by a suitable object boundary criterion. We propose an improved search procedure that is more robust against outlier configurations in the boundary target points by requiring subsequent shape changes to be smooth, which is imposed by a smoothness constraint on the displacement of neighbouring target points at each iteration and implemented by a minimal cost path approach. We compare the original ASM search method and our improved search algorithm with a third method that does not rely on iteratively refined target point positions, but instead optimizes a global Bayesian objective function derived from statistical a priori contour shape and image models. Extensive validation of these methods on a database containing more than 400 images of the femur, humerus and calcaneus using the manual expert segmentation as ground truth shows that our minimal cost path method is the most robust. We also evaluate various measures for capturing local image appearance around each boundary point and conclude that the Mahalanobis distance applied to normalized image intensity profiles extracted normal to the shape is the most suitable criterion among the tested ones for guiding the ASM optimization.