Elastic contour-based image segmentation using genetic algorithms

Medical images are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease evolution. Medical imaging mainly processes uncertain, missing, ambiguous, complementary, inconsistent, redundant contradictory, distorted data, and information has a strong structural character. This paper reports a new semi-automated and supervised method for the segmentation of medical images by using elastic contour (`snake´) model and a well known soft computing technique - genetic algorithms. Promising results show the superiority of this hybrid approach over the best traditional techniques in terms of segmentation errors.