Image segmentation by stochastically relaxing contour fitting

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Abstract
Contour fitting in image segmentation guarantees the closedness of the segment boundary at any stage of the approximation, thus preserving an important global property of the segment. A contour fitting scheme consists of strategies to modify the contour and to optimize the current approximation. For the purpose of contour modification a two-dimensional adaptation of a geometrically deformable model (GDM) is employed. A GDM is a polygon being placed into a structure to be segmented and being deformed until it adequately matches the segment's boundary. Deformation occurs by vertex translation and by introducing new vertices. Sufficient boundary resemblance is achieved by choosing vertex locations in such way that a function is optimized whose different terms describe features attributed to the segment or its boundary. In order to find ideal vertex locations, a stochastic optimisation method is applied which is able to avoid termination of the deformation process in a local optimum (caused, e.g., by noise or artefacts). The deformation terminates after segment boundary and GDM are sufficiently similar. Missing boundary parts between vertices are detected by a path searching technique in a graph whose nodes represent pixel locations. The segmentation algorithm was found to be versatile and robust in the presence of noise being able to segment artificial as well as real image data.

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