<title>Hybrid boundary-based and region-based deformable models for biomedical image segmentation</title>

Abstract

The problem of segmenting an image into visually sensible regions has received considerable attention. Recent techniques based on deformable models show particular promise for this problem because they produce smooth closed object boundaries. These techniques can be broadly classified into two categories: boundary-based deformable models, and region-based deformable models. Both of these approaches have distinct advantages and disadvantages. In this paper, we introduce a hybrid deformable modeling technique which combines the advantages of both approaches and avoids some of their disadvantages. This is accomplished by first minimizing a region-based functional to obtain initial edge strength estimates. Smooth closed object boundaries are then obtained by minimizing a boundary-based functional which is attracted to the initial edge locations. In this paper, we discuss the theoretical advantages of this hybrid approach over existing image segmentation methods and show how this technique can be effectively implemented and used for the segmentation of 2D biomedical images.