Abstract
In this paper, we present an adaptive spatially-constrained deterministic annealing (ASDA) algorithm, which takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image pixels, for image segmentation. The local spatial continuity constraint reduces the noise effect and the classification ambiguity. More importantly, the strength of spatial constraint for each given image pixel is auto-selected by the scaled variance of its neighbor pixels, which results in the adaptiveness of the presented algorithm. The effectiveness and efficiency of the presented method for image segmentation are supported by experimental results on synthetic and MR images.
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