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

机译:具有先前信息统一登记,分割和偏向脑MRI的偏置场校正的模糊框架

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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:除了MRI数据集的偏置场校正之外,本文还为同时分割和注册介绍了一个模糊框架。该框架利用现有信息,该信息可以通过数据集进行组织的均值和组织分布。此外,它适用于没有任何对数转换的给定的原始图像强度,从而产生更准确的结果和更快的性能。使用模拟和真实脑MRI数据进行评估算法。结果表明,该算法确实提高了分割精度。

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