Segmentation of Brain MR Images using Modified Fuzzy c-means clustering with a genetically optimized approach

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This paper presents a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity based classification. 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 GA to optimize the modified fuzzy (J_m) c-means function. The genetic algorithm approach is able to find the lowest known J_m value or a J_m associated with a partition very similar to that associated with the lowest J_m value. The performance of the algorithm is evaluated on a series of MR images of the brain.