Segmentation of brain MR images using genetically guided clustering

This paper presents a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. The proposed algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel to be influenced by the labels in its immediate neighborhood. Clustering algorithms such as FCM that use calculus based optimization methods can be trapped by local extrema in the process of optimizing the clustering criterion. They are also very sensitive to initialization. The proposed algorithm uses Genetic Algorithm (GA) to optimize the modified fuzzy c-means function. The performance of the algorithm is evaluated on a series of MR images of the brain.