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Consistent segmentation using a Rician classifier

机译：使用Rician分类器进行一致的细分

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Several popular classification algorithms used to segment magnetic resonance brain images assume that the image intensities, or log-transformed intensities, satisfy a finite Gaussian mixture model. In these methods, the parameters of the mixture model are estimated and the posterior probabilities for each tissue class are used directly as soft segmentations or combined to form a hard segmentation. It is suggested and shown in this paper that a Rician mixture model fits the observed data better than a Gaussian model. Accordingly, a Rician mixture model is formulated and used within an expectation maximization (EM) framework to yield a new tissue classification algorithm called Rician Classifier using EM (RiCE). It is shown using both simulated and real data that RiCE yields comparable or better performance to that of algorithms based on the finite Gaussian mixture model. As well, we show that RiCE yields more consistent segmentation results when used on images of the same individual acquired with different T1-weighted pulse sequences. Therefore, RiCE has the potential to stabilize segmentation results in brain studies involving heterogeneous acquisition sources as is typically found in both multi-center and longitudinal studies.
机译：用于分割磁共振脑图像的几种流行的分类算法假设图像强度或对数转换强度满足有限高斯混合模型。在这些方法中，估计混合模型的参数，并将每个组织类别的后验概率直接用作软分割或组合以形成硬分割。本文提出并表明，Rician混合模型比高斯模型更适合观测数据。因此，制定了Rician混合模型并在期望最大化（EM）框架内使用，以产生一种新的组织分类算法，称为使用EM的Rician分类器（RiCE）。使用模拟数据和实际数据都可以证明，RiCE的性能与基于有限高斯混合模型的算法相当或更好。同样，我们显示，当在以不同T1加权脉冲序列采集的同一个人的图像上使用RiCE时，RiCE会产生更一致的分割结果。因此，RiCE有潜力在涉及异源采集源的脑部研究中稳定分割结果，这在多中心和纵向研究中都是常见的。

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