Genetic Approach on Medical Image Segmentation by Generalized Spatial Fuzzy C- Means Algorithm

Medical Image segmentation is an important tool in viewing and analyzing magnetic resonance (MR) images and solving a wide range of problems in medical imaging. The Fuzzy C means clustering algorithm performs well in the absence of noise as well as it considers only the pixel attributes and not its neighbors. This leads to accuracy degradation in the image segmentation process. This can be addressed by using Generalized spatial Fuzzy C-means clustering algorithm (GSFCM), which utilizes both given pixel attributes and the spatial local information. This algorithm corresponds to the weights of the neighbor elements based on their distance attributes. Though GSFCM gives good output, the main drawback behind this method is the inability of generating global minima for the objective function. To improve the efficiency of this clustering algorithm, this paper proposes the genetic algorithm (GA) based GSFCM algorithm called GAGSFCM. By using GAGSFCM, the global minima of the clustering objective function can be reached. Although this algorithm has high computational complexity, it greatly improves the accuracy of the segmentation on medical images.