Prostate segmentation on pelvic CT images using a genetic algorithm

A genetic algorithm (GA) for automating the segmentation of the prostate on pelvic computed tomography (CT) images is presented here. The images consist of slices from three-dimensional CT scans. Segmentation is typically performed manually on these images for treatment planning by an expert physician, who uses the "learned" knowledge of organ shapes, textures and locations to draw a contour around the prostate. Using a GA brings the flexibility to incorporate new "learned" information into the segmentation process without modifying the fitness function that is used to train the GA. Currently the GA uses prior knowledge in the form of texture and shape of the prostate for segmentation. We compare and contrast our algorithm with a level-set based segmentation algorithm, thereby providing justification for using a GA. Each individual of the GA population represents a segmenting contour. Shape variability of the prostate derived from manually segmented images is used to form a shape representation from which an individual of the GA population is randomly generated. The fitness of each individual is evaluated based on the texture of the region it encloses. The segmenting contour that encloses the prostate region is considered more fit than others and is more likely to be selected to produce an offspring over successive generations of the GA run. This process of selection, crossover and mutation is iterated until the desired region is segmented. Results of 2D and 3D segmentation are presented and future work is also discussed here.