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3D Segmentation of MR Brain Images into White Matter, Gray Matter and Cerebro-Spinal Fluid by Means of Evidence Theory

机译:利用证据理论将MR脑图像3D分割为白色,灰色和脑脊髓液

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We propose an original scheme for the 3D segmentation of multi-echo MR brain images into white matter, gray matter and cerebro-spinal fluid. To take into account complementary, redundancy and eventual conflicts provided by the different echoes, a fusion process based on Evidence theory is used. Such theory, well suited to imprecise and uncertain data, provides great fusion tools. The originality of our method is to include a regularization process by the mean of Dempster's combination. Adding neighborhood information increases the knowledge. The segmentation is more confident, accurate and efficient. The method is applied to simulated multi-echo data and compared with method based on Markov Random Field theory. The results are very encouraging and show that Evidence theory is well suited to such problematic.
机译:我们提出了一种将多回波MR脑图像3D分割为白质,灰质和脑脊髓液的原始方案。为了考虑到不同回波提供的互补,冗余和最终冲突,使用了基于证据理论的融合过程。这种理论非常适合不精确和不确定的数据,提供了很好的融合工具。我们的方法的独创性是通过Dempster的组合包括正则化过程。添加邻域信息可增加知识。细分更加自信,准确和高效。将该方法应用于模拟的多回波数据,并与基于马尔可夫随机场理论的方法进行了比较。结果非常令人鼓舞,表明证据理论非常适合此类问题。

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