earticle

영어

Segmentation of brain tissues is one important process prior to many analyses and visualization tasks for magnetic resonance (MR) images. Clustering is one of the unsupervised techniques for doing the segmentation. Fuzzy clustering techniques have not been applied for single-channel MR images although they have shown promise in segmentation of multichannel MR images. Unfortunately, MR images always contain significant quantity of noise caused by operator performance, equipment and the environment. This noise could lead to serious inaccuracies in the segmentation result. We conduct the research in measuring the performance of fuzzy clustering algorithms over crisp clustering algorithms in different noise level for single-channel MR image. To validate the accuracy and robustness of the result of clustering algorithms we carried out experiments on simulated MR brain scans. The performance of algorithms is analyzed form three measures namely: number of iterations required, misclassification error and per class (tissue) misclassification error in different noise level present in the single-channel MR image. As, clustering is done based on some distance measure, we also compare the performance of clustering algorithms based on distance norm used for it.