Abstract
This paper describes a process for improving the segmentation of brain magnetic resonance (MR) images. It involves two stages; preprocessing and segmentation. During preprocessing, the image intensities are first standardized using the pixel histograms. Morphological processing is then used to remove the non-brain regions. During the segmentation process, normal and abnormal brain tissues are segmented using both the traditional fuzzy c-means (FCM) clustering algorithm, and a new improved FCM algorithm. Neighborhood effects are considered in the latter method to overcome noise. Segmentation results show that this method is more robust to noise and can improve the integrity of the segmentation performance.
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