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

机译:基于改进的C的脑组织自动分割C算法聚类算法

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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.
机译:在医学图像中,存在通常很多噪音,噪音会严重影响分割结果的准确性。在图像分割中的传统标准模糊C-mance(FCM)算法不会考虑相邻像素的关系,这导致标准模糊C-mance(FCM)算法对图像中的噪声非常敏感。提出了改进的CFUZY C-MATION(FCM)算法,将本地和非本地信息占用成标准模糊C型(FCM)聚类算法。前消化结果可以表明,改进的算法可以达到比脑组织分割的其他方法更好的效果。

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