Probabilistic Fitting of Active Shape Models

Abstract

Active Shape Models (ASMs) are a classical and widely used approach for fitting shape models to images. In this paper, we propose a fully probabilistic interpretation of ASM fitting as Bayesian inference. To infer the posterior, we use the Metropolis-Hastings algorithm. We then use the maximum a posteriori sample as the segmentation result. Our approach has several advantages compared to classical ASM fitting: (1) We are left with fewer parameters that we need to choose. (2) It is less prone to get trapped in local minima. (3) It becomes straightforward to extend the approach to include additional information, such as expert annotations. (4) It is even simpler to implement than the classical ASM fitting method. We apply our algorithm to the SLIVER dataset and show that it achieves a higher segmentation accuracy than the standard ASM approach. We further demonstrate the flexibility and expressivity of the framework by integrating experts annotations along parts of the outline to further increase the accuracy. The code used for fitting is based on open-source software and made available to the community.