Segmentation of 3d Medical Images through Genetically-optimized Contour-tracking Algorithms

In this paper we describe a method for evolving adaptive procedures for the segmentation of anatomical structures in 3D medical data sets. With our method, the user rst manually traces one or more 2D contours of a structure of interest on parallel planes arbitrarily cutting the data set. Such contours are then used as training examples for a genetic algorithm to evolve a contour detector which can later be applied either to the rest of the sequence or to other image sequences to obtain a full segmentation of the structure of interest. Segmentation is driven by a contour-tracking strategy based on an elastic-contour model that is also optimized by the genetic algorithm. We report results obtained on a software-generated phantom and on actual tomographic images of diierent sorts.

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