2D Segmentation Using a Robust Active Shape Model With the EM Algorithm

Statistical shape models have been extensively used in a wide range of applications due to their effectiveness in providing prior shape information for object segmentation problems. The most popular method is the active shape model (ASM). However, accurately fitting the shape model to an object boundary under a cluttered environment is a challenging task. Under such assumptions, the model is often attracted toward invalid observations (outliers), leading to meaningless estimates of the object boundary. In this paper, we propose a novel algorithm that improves the robustness of ASM in the presence of outliers. The proposed framework assumes that both type of observations (valid observations and outliers) are detected in the image. A new strategy is devised for treating the data in different ways, depending on the observations being considered as valid or invalid. The proposed algorithm assigns a different weight to each observation. The shape parameters are recursively updated using the expectation-maximization method, allowing a correct and robust fit of the shape model to the object boundary in the image. Two estimation criteria are considered: 1) the maximum likelihood criterion and 2) the maximum a posteriori criterion that use priors for the unknown parameters. The methods are tested with synthetic and real images, comprising medical images of the heart and image sequences of the lips. The results are promising and show that this approach is robust in the presence of outliers, leading to a significant improvement over the standard ASM and other state-of-the-art methods.