Robust Statistical Prior Knowledge for Active Contours Prior Knowledge for Active Contours

We propose in this paper a new method of active contours with statistical shape prior. The presented approach is able to manage situations where the prior knowledge on shape is unknown in advance and we have to construct it from the available training data. Given a set of several shape clusters, we use a set of complete, stable and invariants shape descriptors to represent shape. A Linear Discriminant Analysis (LDA), based on Patrick-Fischer criterion, is then applied to form a distinct clusters in a low dimensional feature subspace. Feature distribution is estimated using an Estimation-Maximization (EM) algorithm. Having a currently detected front, a Bayesian classifier is used to assign it to the most probable shape cluster. Prior knowledge is then constructed based on it's statistical properties. The shape prior is then incorporated into a level set based active contours to have satisfactory segmentation results in presence of partial occlusion, low contrast and noise.