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Spatial transformation and registration of brain images using elastically deformable models.
Computer Vision and Image Understanding ( IF 4.3 ) Pub Date : 1997-05-01 , DOI: 10.1006/cviu.1997.0605
C Davatzikos 1
Affiliation  

The development of algorithms for the spatial transformation and registration of tomographic brain images is a key issue in several clinical and basic science medical applications, including computer-aided neurosurgery, functional image analysis, and morphometrics. This paper describes a technique for the spatial transformation of brain images, which is based on elastically deformable models. A deformable surface algorithm is used to find a parametric representation of the outer cortical surface and then to define a map between corresponding cortical regions in two brain images. Based on the resulting map, a three-dimensional elastic warping transformation is then determined, which brings two images into register. This transformation models images as inhomogeneous elastic objects which are deformed into registration with each other by external force fields. The elastic properties of the images can vary from one region to the other, allowing more variable brain regions, such as the ventricles, to deform more freely than less variable ones. Finally, the framework of prestrained elasticity is used to model structural irregularities, and in particular the ventricular expansion occurring with aging or diseases, and the growth of tumors. Performance measurements are obtained using magnetic resonance images.

中文翻译:

使用弹性可变形模型对大脑图像进行空间变换和配准。

断层扫描大脑图像的空间转换和配准算法的开发是若干临床和基础科学医学应用(包括计算机辅助神经外科,功能图像分析和形​​态计量学)中的关键问题。本文介绍了一种基于弹性可变形模型的大脑图像空间转换技术。可变形表面算法用于查找皮质外部表面的参数表示,然后在两个大脑图像中的相应皮质区域之间定义映射。根据生成的图,然后确定三维弹性变形变换,该变换将两个图像对齐。此变换将图像建模为不均匀的弹性对象,这些对象在外力场的作用下变形为彼此对齐。图像的弹性性质可以从一个区域到另一个区域变化,从而使更多可变的大脑区域(例如心室)比可变性较小的区域更自由地变形。最后,使用预应力弹性框架来建模结构不规则,尤其是随着年龄增长或疾病而出现的心室扩张以及肿瘤的生长。使用磁共振图像获得性能测量结果。预应力弹性的框架用于建模结构不规则,尤其是随着年龄增长或疾病而出现的心室扩张以及肿瘤的生长。使用磁共振图像获得性能测量结果。预应力弹性的框架用于建模结构不规则,尤其是随着年龄增长或疾病而出现的心室扩张以及肿瘤的生长。使用磁共振图像获得性能测量结果。
更新日期:2019-11-01
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