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3D Method of Using Spatial-Varying Gaussian Mixture and Local Information to Segment MR Brain Volumes

机译:使用空间变化高斯混合物和局部信息分割MR脑体积的3D方法

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The paper is an extension of previous work on spatial-varying Gaussian mixture and Markov random field (SVGM-MRF) from 2D to 3D to segment the MR brain volume with the presence of noise and inhomogeneity. The reason for this extension is that MR brain data are naturally three dimensional, and the information from the additional dimension provides a more accurate conditional probability representation. To reduce large computation time and memory requirements for 3D implementation, a method of using only the local window information to perform the necessary parameter estimations and to achieve the tissue labeling is proposed. The experiments on fifteen brain volumes with various noise and inhomogeneity levels and comparisons with other three well-known 2D methods are provided. The new method outperforms all three 2D methods for high noise and inhomogeneity data which is a very common occurrence in MR applications.
机译:本文是对先前的时空变化高斯混合和马尔可夫随机场(SVGM-MRF)从2D到3D的工作的扩展,以分割存在噪声和不均匀性的MR脑体积。这种扩展的原因是MR脑数据自然是三维的,而来自附加维度的信息则提供了更准确的条件概率表示。为了减少3D实现的大量计算时间和存储器需求,提出了一种仅使用局部窗口信息来执行必要的参数估计并实现组织标记的方法。提供了在15种具有各种噪声和不均匀性水平的大脑体积上的实验,以及与其他三种众所周知的2D方法的比较。对于高噪声和非均匀性数据,新方法优于所有三种2D方法,这在MR应用中非常常见。

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