Volume 20, Issue 1

The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation-no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. Here, the authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. The authors show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, the authors show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.

有限混合(FM)模型是最常用的脑部磁共振(MR)图像统计分割模型，因为其数学形式简单且理想脑MR图像具有分段常数特性。然而，作为基于直方图的模型，FM有一个固有的局限性——未考虑空间信息。这导致FM模型仅适用于噪声水平低的明确定义图像；不幸的是，由于部分容积效应和偏置场失真等伪影，情况往往并非如此。在这些条件下，基于FM模型的方法会产生不可靠的结果。本文作者提出了一种新颖的隐马尔可夫随机场(HMRF)模型，这是一种由MRF生成的随机过程，其状态序列无法直接观测，但可以通过观测间接估计。从数学上可以证明，FM模型是HMRF模型的退化形式。HMRF模型的优势在于通过相邻位置的相互影响来编码空间信息的方式。尽管其他研究者已将MRF建模应用于MR图像分割，但大多数报道的方法仅限于在基于FM模型的方法中将MRF作为通用先验。为了拟合HMRF模型，使用了EM算法。作者表明，通过将HMRF模型和EM算法结合到HMRF-EM框架中，可以实现准确且鲁棒的分割。更重要的是，HMRF-EM框架可以轻松与其他技术结合。例如，作者展示了如何将Guillemaud和Brady(1997)的偏置场校正算法纳入该框架，从而实现脑MR图像分割的三维全自动方法。

Highly accurate surface models of the cerebral cortex are becoming increasingly important as tools in the investigation of the functional organization of the human brain. The construction of such models is difficult using current neuroimaging technology due to the high degree of cortical folding. Even single voxel mis-classifications can result in erroneous connections being created between adjacent banks of a sulcus, resulting in a topologically inaccurate model. These topological defects cause the cortical model to no longer be homeomorphic to a sheet, preventing the accurate inflation, flattening, or spherical morphing of the reconstructed cortex. Surface deformation techniques can guarantee the topological correctness of a model, but are time-consuming and may result in geometrically inaccurate models. In order to address this need the authors have developed a technique for taking a model of the cortex, detecting and fixing the topological defects while leaving that majority of the model intact, resulting in a surface that is both geometrically accurate and topologically correct.

高精度的大脑皮层表面模型作为研究人脑功能组织的工具变得越来越重要。由于皮层高度折叠，使用当前的神经影像技术构建此类模型非常困难。即使单个体素的错误分类也可能导致相邻脑回之间产生错误连接，从而产生拓扑不准确的模型。这些拓扑缺陷使得皮层模型不再与曲面片同胚，从而阻止了重建皮层的精确膨胀、展平或球形变形。表面变形技术可以保证模型的拓扑正确性，但耗时且可能导致几何不准确的模型。为了解决这一需求，作者开发了一种技术，用于获取皮层模型，检测并修复拓扑缺陷，同时保留模型的大部分结构，从而得到既几何精确又拓扑正确的表面。

Three-dimensional (3-D) imaging of the heart is a rapidly developing area of research in medical imaging. Advances in hardware and methods for fast spatio-temporal cardiac imaging are extending the frontiers of clinical diagnosis and research on cardiovascular diseases. In the last few years, many approaches have been proposed to analyze images and extract parameters of cardiac shape and function ...

心脏的三维（3-D）成像是医学成像中一个快速发展的研究领域。快速时空心脏成像的硬件和方法的进步正在拓展心血管疾病临床诊断和研究的边界。在过去几年中，许多方法被提出用于分析图像和提取心脏形状与功能的参数……

This paper presents an original method for three-dimensional elastic registration of multimodal images. The authors propose to make use of a scheme that iterates between correcting for intensity differences between images and performing standard monomodal registration. The core of the authors' contribution resides in providing a method that finds the transformation that maps the intensities of one...

本文提出了一种原创的三维多模态图像弹性配准方法。作者提出利用一种方案，在纠正图像间的强度差异和执行标准单模态配准之间迭代进行。作者贡献的核心在于提供了一种方法，能够找到将一个图像的强度映射到另一个图像的变换。

In the last half decade, fast methods of magnetic resonance imaging have led to the possibility, for the first time, of noninvasive dynamic brain imaging. This has led to an explosion of work in the Neurosciences. From a signal processing viewpoint the problems are those of nonlinear spatio-temporal system identification. Here, the authors develop new methods of identification using novel spatial ...

在过去的五年中，快速的磁共振成像方法首次使得无创动态脑成像成为可能，这引发了神经科学领域的研究热潮。从信号处理的角度来看，问题在于非线性时空系统辨识。本文作者利用新颖的空间...发展了新的辨识方法。

The authors propose a method for restoring the underlying true signal in noisy functional images. The Nadaraya-Watson (NW) estimator described in, e.g., G. S. Watson, "Smooth regression analysis," Sankhya Series A, vol. 26, p. 101-16 (1964) is a classical nonparametric estimator for this problem. Since the true scene in many applications contains abrupt changes between pixels of different types, a...

作者提出了一种恢复含噪功能图像中潜在真实信号的方法。Nadaraya-Watson (NW) 估计器，例如见 G. S. Watson, "Smooth regression analysis," Sankhya Series A, vol. 26, p. 101-16 (1964)，是解决此问题的经典非参数估计器。由于许多应用中真实场景在不同类型的像素之间存在突变...