Tomographic reconstruction of circular and elliptical objects using genetic algorithm

This paper addresses the model-based tomographic reconstruction from a limited number of noisy projections for the detection and estimation of multiple circular and elliptical objects of known intensities placed on a known uniform background. As the direct computation of the maximum-likelihood estimate is impractical even for small number of objects in the image, we have used an optimization strategy based on the genetic algorithm (GA). The GA uses variable-length chromosome coding and two knowledge-based operators namely, add object and delete object. The proposed algorithm correctly detects the number of objects in the given images and the estimation of the object´s parameters, namely, the location, size and shape is fairly accurate.