Registration.
Registration.
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DOI：
10.1007/978-1-4939-7647-8_1
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Joshi,AnandA
中科院分区：
文献类型：
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作者：
Joshi,AnandA
图像配准的目标是在两个图像（主题图像和目标图像）之间找到1-1点的对应关系。了解两个大脑图像之间的点对点对应关系，可以比较结构和功能成像数据，如感兴趣的区域、功能数据（例如，fMRI、EEG、MEG、DTI）和几何形状。图像配准过程还允许创建概率解剖图谱(Mazziotta et al., Neuroimage 2(2): 89-101, 1995；李志强，刘志强。计算机辅助设计[J] .计算机工程学报，2009 (4):567-581；Thompson等人，使用皮质模式匹配和基于人群的概率脑图谱检测疾病特异性脑结构模式。: IPMI2001。计算机科学课堂讲稿，第488-501页，2001)，通过标签转移、情态融合、形态分析的自动分割（Hua, Neuroimage 43(3): 458-469, 2008），以及许多其他应用。图像配准技术力求在对象和目标图像之间找到一对一的对应关系来完成这一任务。这种对应关系由光滑变形场定义。这个变形场捕获了两幅图像的几何变化。在本章中，我们将回顾各种专门为人脑设计的图像配准技术。
The goal of image registration is to find a 1-1 point-wise correspondence between two images, a subject image and a target image. Knowing the pointwise correspondence between two brain images allows comparison of structural and functional imaging data such as regions of interest, functional data (e.g., fMRI, EEG, MEG, DTI), and geometric shapes. The image registration process also allows creation of probabilistic anatomical atlases (Mazziotta et al., Neuroimage 2(2):89–101, 1995; Thompson, J Comput Assist Tomogr 21(4):567–581, 1997; Thompson et al., Detecting disease-specific patterns of brain structure using cortical pattern matching and a population-based probabilistic brain atlas. In: IPMI2001. Lecture notes in computer science, pp 488–501, 2001), automatic segmentation by label transfer, modality fusion, morphological analysis (Hua, Neuroimage 43(3):458–469, 2008), and many other applications. Image registration techniques strive to find a one-to-one correspondence between subject and target images to perform this task. This correspondence is defined by a smooth deformation field. This deformation field captures the geometric variations in the two images. In this chapter, we will review various techniques for image registration that are specifically designed for human brain.