segmentation of medical images using a genetic algorithm
Segmentation of medical images is challenging due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. Consequently, this task involves incorporating as much prior information as possible (e.g., texture, shape, and spatial location of organs) into a single framework. In this paper, we present a genetic algorithm for automating the segmentation of the prostate on two-dimensional slices of pelvic computed tomography (CT) images. In this approach the segmenting curve is represented using a level set function, which is evolved using a genetic algorithm (GA). Shape and textural priors derived from manually segmented images are used to constrain the evolution of the segmenting curve over successive generations.We review some of the existing medical image segmentation techniques. We also compare the results of our algorithm with those of a simple texture extraction algorithm (Laws' texture measures) as well as with another GA-based segmentation tool called GENIE. Our preliminary tests on a small population of segmenting contours show promise by converging on the prostate region. We expect that further improvements can be achieved by incorporating spatial relationships between anatomical landmarks, and extending the methodology to three dimensions.
genetic algorithms level set methods texture segmentation