Medical image interpretation: a generic approach using deformable templates

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Abstract
We describe a generic approach to image interpretation, based on combining a general method of building flexible template models with genetic algorithm (GA) search. The method can be applied to a given image interpretation problem simply by training a statistical shape model, using a set of examples of the image structure to be located. A local optimization technique has been incorporated into the GA search and shown to improve the speed of convergence and optimality of solution. We present results from three medical applications, demonstrating that the new method offers significant improvements when compared with previously reported approaches to flexible template matching, particularly the ability to deal with different domains of application using a standard method and the possibility of employing complex multipart models. We also describe how the method can be simply extended to track structures in image sequences and segment three dimensional objects in volume images.

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