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Styner, M., Brechbuhler, C. and Szckely, G. (2000) Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Transactions on Medical Imaging, 19, 153-165. doi:10.1109/42.845174

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• TITLE: Automatic segmentation of brain tissue based on improvedfuzzy c means clustering algorithm

  AUTHORS: Zhuang Miao, Xiaomei Lin, Chengcheng Liu

  KEYWORDS: Local Information; Non-local Mean; Brain Tissue Segmentation

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.4 No.2, February 25, 2011

  ABSTRACT: In medical images, exist often a lot of noise, the noise will seriously affect the accuracy of the segmentation results. The traditional standard fuzzy c-means(FCM) algorithm in image segmentation do not taken into account the relationship the adjacent pixels, which leads to the standard fuzzy c-means(FCM) algorithm is very sensitive to noise in the image. Proposed improvedfuzzy c-means(FCM) algorithm, taking both the local and non-local information into the standard fuzzy c-means(FCM) clustering algorithm. The ex-periment results can show that the improved algorithm can achieve better effect than other methods of brain tissue segmentation.