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Automatic segmentation and classification of human brain image based on a fuzzy brain atlas

机译:基于模糊脑图谱的人脑图像自动分割与分类

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Abstract: It is difficult to automatically segment and classify tomograph images of actual patient's brain. Therefore, many interactive operations are performed. It is very time consuming and its precision is much depended on the user. In this paper, we combine a brain atlas and 3D fuzzy image segmentation into the image matching. It can not only find out the precise boundary of anatomic structure but also save time of the interactive operation. At first, the anatomic information of atlas is mapped into tomograph images of actual brain with a two step image matching method. Then, based on the mapping result, a 3D fuzzy structure mask is calculated. With the fuzzy information of anatomic structure, a new method of fuzzy clustering based on genetic algorithm is used to segment and classify the real brain image. There is only a minimum requirement of interaction in the whole process, including removing the skull and selecting some intrinsic point pairs.!6
机译:摘要:很难对实际患者的大脑断层图像进行自动分割和分类。因此,执行许多交互操作。这非常耗时,其精度在很大程度上取决于用户。在本文中,我们将脑图集和3D模糊图像分割结合到图像匹配中。它不仅可以找到解剖结构的精确边界,而且可以节省交互操作的时间。首先,利用两步图像匹配方法将地图集的解剖信息映射到实际大脑的断层图像中。然后,基于映射结果,计算3D模糊结构掩模。利用解剖结构的模糊信息,基于遗传算法的模糊聚类新方法被用于对真实的大脑图像进行分割和分类。在整个过程中,交互的最低要求是,包括移除头骨和选择一些内在的点对!! 6

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