<title>Segmentation using atlas-guided deformable contours</title>

Abstract

Deformable models using energy minimization have proven to be useful in computer vision for segmenting complex objects based on various measures of image contrast. In this paper, we incorporate prior shape knowledge to aid boundary finding of 2D objects in an image in order to overcome problems associated with noise, missing data, and the overlap of spurious regions. The prior shape knowledge is encoded as an atlas of contours of default shapes of known objects. The atlas contributes a term in an energy function driving the segmenting contour to seek a balance between image forces and conformation to the atlas shape. The atlas itself is allowed to undergo a cost free affine transformation. An alternating algorithm is proposed to minimize the energy function and hence achieve the segmentation. First, the segmenting contour deforms slightly according to image forces, such as high gradients, as well as the atlas guidance. Then the atlas is itself updated according to the current estimate of the object boundary by deforming through an affine transform to optimally match the boundary. In this way, the atlas provides strong guidance in some regions that would otherwise be hard to segment. Some promising results on synthetic and real images are shown.