Pattern Recognition and Image Analysis
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In this paper, we propose a segmentation algorithm which combines the ideas from local watershed transforms and the region based deformable models. Traditionally, watersheds are computed in the whole image and then some region merging techniques are applied on them to reach the segmentation of structures. Here, we propose that watershed regions can be used as operators in region-based deformable models. These regions are computed only when the deformable models reach them. Then, they are added to (or subtracted from) the deformable models via a measure computed from two terms: (i) statistical fit of regions to the models, region competition; (ii) smoothness of such fits, smoothness constraint. The proposed algorithm is computationally efficient because it operates on regions instead of pixels. In addition, this algorithm allows better boundary localization due to the edge information brought by watersheds. Moreover, the proposed algorithm can handle topological changes, e.g., split or merge, during the evolutions without an additional embedded surface as in the case of level set formulation. Furthermore, structure-based smoothness of segmented objects is obtained by using the smoothness term computed from the alignment of regions. We illustrate the efficiency and accuracy of the proposed technique on several medical data such as MRA and CTA data.