A Novel Efficient Medical Image Segmentation Methodology

Image segmentation plays a crucial role in many medical applications. The threshold based medical image segmentation approach is the most common and effective method for medical image segmentation, but it has some shortcomings such as high complexity, poor real time capability and premature convergence, etc. To address above issues, an improved evolution strategies is proposed to use for medical image segmentation, there are 2 populations concurrently during evolution, one focuses on local search in order to search solutions near optimal solution, and the other population that implemented based on chaotic theory focuses on global search so as to keep the variety of individuals and jump out from the local maximum to overcome the problem of premature convergence. The encoding scheme, fitness function, and evolution operators are also designed. The experimental results validated the effectiveness and efficiency of the proposed approach.