A model-based, semi-global segmentation approach for automatic 3-D point landmark localization in neuroimages
A model-based, semi-global segmentation approach for automatic 3-D point landmark localization in neuroimages
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DOI：
10.1109/tmi.2008.915684
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发表时间：
2008-08-01
影响因子：
10.6
通讯作者：
Nowinski, Wieslaw L.
中科院分区：
文献类型：
--
作者：
Liu, Jin-Min;Gao, Wenpeng;Nowinski, Wieslaw L.
现有的三维解剖点地标定位方法对噪声敏感，通常会提取大量的伪地标。基于参数模型的方法对于不能用简单的参数形式建模的地标定位来说是不实用的。一些专门的方法使用解剖学知识来识别特定的标志是不够普遍的，以应付其他标志。在本文中，我们提出了一种基于模型的半全局分割方法来自动定位神经图像中的三维点地标。为了定位地标，首先通过活动曲面模型实现半全局分割（即在地标的一定邻域内分割研究结构的一部分），然后仅通过分析被分割的部分来定位地标。该方法结合了模型到图像的全局配准、半全局结构配准、基于活动曲面的分割和锚定曲面配准，使该方法对噪声和形状变化具有鲁棒性。为了评估该方法，我们将其应用于心室标志的定位，包括曲率极值，中心线交叉点和终点。对48张临床和18张模拟磁共振（MR）体积图像进行的实验表明，该方法能够以平均1毫米的精度（即图像分辨率水平）定位这些地标。我们还说明了该方法在皮质地标识别中的应用，并讨论了其潜在的应用范围，从计算机辅助放射学和外科到扫描图谱注册。
The existing differential approaches for localization of 3-D anatomic point landmarks in 3-D images are sensitive to noise and usually extract numerous spurious landmarks. The parametric model-based approaches are not practically usable for localization of landmarks that can not be modeled by simple parametric forms. Some dedicated methods using anatomic knowledge to identify particular landmarks are not general enough to cope with other landmarks. In this paper, we propose a model-based, semi-global segmentation approach to automatically localize 3-D point landmarks in neuroimages. To localize a landmark, the semi-global segmentation (meaning the segmentation of a part of the studied structure in a certain neighborhood of the landmark) is first achieved by an active surface model, and then the landmark is localized by analyzing the segmented part only. The joint use of global model-to-image registration, semi-global structure registration, active surface-based segmentation, and point-anchored surface registration makes our method robust to noise and shape variation. To evaluate the method, we apply it to the localization of ventricular landmarks including curvature extrema, centerline intersections, and terminal points. Experiments with 48 clinical and 18 simulated magnetic resonance (MR) volumetric images show that the proposed approach is able to localize these landmarks with an average accuracy of 1 mm (i.e., at the level of image resolution). We also illustrate the use of the proposed approach to cortical landmark identification and discuss its potential applications ranging from computer-aided radiology and surgery to atlas registration with scans.