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This paper presents a new segmentation approach based on hybridization of the  genetic algorithms (GAs) and seed region growing to produce accurate medical  image segmentation, and to overcome the oversegmentation problem. A new  fitness function is presented for generating global minima of the objective  function, and a chromosome representation suitable for the process of  segmentation is proposed. The proposed approach starts by selecting a set of  data randomly distributed all over the image as initial population. Each  chromosome contains three parts: control genes, graylevels genes, and  position genes. Each gene associates the intensity values by their positions.  The region growing algorithm uses these values as an initial seeds to find  accurate regions for each control gene. The proposed fitness function is used  to evolve the population to find the best region for each control gene.  Chromosomes are updated by applying the operators of GAs to evolve  segmentation results. Applying the proposed approach to real MRI datasets,  better results were achieved compared with the clustering-based fuzzy method.

A novel approach based on genetic algorithms and region growing for magnetic resonance image (MRI) segmentation