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A fuzzy framework with prior information unifying registration, segmentation and bias field correction of brain MRI

机译:具有先验信息的模糊框架统一了大脑MRI的配准,分割和偏场校正

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This paper introduces a fuzzy framework for the simultaneous segmentation and registration in addition to bias field correction of MRI datasets. The framework utilizes prior information which may be available about the tissues' mean intensities and tissues' distribution through the datasets. Moreover it works on the given, original image intensities without any logarithmic transformation and thus produces more accurate results and faster performance. The algorithm is evaluated using simulated and real brain MRI data. The results show that the algorithm has indeed improved the segmentation accuracy.
机译:本文介绍了一种用于同时分割和配准的模糊框架,以及MRI数据集的偏差场校正。该框架利用通过数据集可获得的有关组织的平均强度和组织分布的先验信息。而且,它可以在给定的原始图像强度下工作,而无需任何对数转换,因此可以产生更准确的结果和更快的性能。该算法使用模拟和真实的大脑MRI数据进行评估。结果表明,该算法确实提高了分割精度。

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