Shen D, Herskovits E H, Davatzikos C
Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA.
IEEE Trans Med Imaging. 2001 Apr;20(4):257-70. doi: 10.1109/42.921475.
This paper presents a deformable model for automatically segmenting brain structures from volumetric magnetic resonance (MR) images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via a set of affine-invariant attribute vectors, each of which characterizes the geometric structure around a point of the model from a local to a global scale. The attribute vectors, in conjunction with the deformation mechanism of the model, warranty that the model not only deforms to nearby edges, as is customary in most deformable surface models, but also that it determines point correspondences based on geometric similarity at different scales. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and gradually shifts focus to other structures as those become closer to their respective targets and, therefore, more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images.
本文提出了一种可变形模型，用于从体积磁共振（MR）图像中自动分割脑结构并获得点对应关系，该模型在分层方案中使用几何和统计信息。几何信息通过一组仿射不变属性向量嵌入到模型中，每个属性向量从局部到全局尺度表征模型中某一点周围的几何结构。这些属性向量与模型的变形机制相结合，确保模型不仅像大多数可变形表面模型那样向附近边缘变形，而且还能基于不同尺度的几何相似性确定点对应关系。所提出的模型具有自适应性，因为它最初聚焦于最可靠的感兴趣结构，随着其他结构更接近各自目标并因此变得更可靠，逐渐将焦点转移到这些结构上。所提出的技术已用于从体积MR图像中分割脑室、尾状核和豆状核的边界。
