Image Analysis and Communications Laboratory, Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Med Image Anal. 2012 Feb;16(2):524-35. doi: 10.1016/j.media.2011.12.001. Epub 2011 Dec 13.
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.
几种常用的磁共振脑图像分割分类算法假设图像强度或对数变换后的强度满足有限的高斯混合模型。在这些方法中，混合模型的参数被估计，组织类别的后验概率被直接用作软分割，或组合形成硬分割。本文提出并证明了瑞利混合模型比高斯模型更适合观测数据。因此，提出了一种瑞利混合模型，并在期望最大化 (EM) 框架内使用它来产生一种新的组织分类算法，称为使用 EM 的瑞利分类器 (RiCE)。使用模拟和真实数据表明，RiCE 的性能与基于有限高斯混合模型的算法相当或更好。此外，我们还表明，当对使用不同 T1 加权脉冲序列获得的同一个体的图像进行使用时，RiCE 会产生更一致的分割结果。因此，RiCE 有可能稳定涉及不同采集源的脑研究中的分割结果，这在多中心和纵向研究中通常都可以找到。
