Abstrait

Combining Evolutionary Algorithms and Average Overlap Metric Rules for Medical Image Segmentation

M. A. Abdallah, Ashraf Afifi, E. A .Zanaty

In this paper, we explore a new algorithm based on evolutionary algorithms and fusion concepts for improving medical image segmentation. The proposed approach starts by finding seeds that cover the image using genetic algorithm (GA). This initial partition is used as the seed to a computationally efficient region growing method to produce the closed regions. The average overlap metric (AOM) is used to classify these regions into groups based on the similarity criterion. The fusion modules are applied to each group to find the points that label the suite membership values. The different fusion rules will be applied to these groups to produce a set of chromosomes to select the best data in each chromosome to represent the final segment. To prove the efficiency of the proposed algorithm, the proposed algorithm will be applied to challenging applications: MRI datasets, 3D simulated MRIs, and gray matter/white matter of brain segmentations.