Sotiras Aristeidis, Ou Yangming, Glocker Ben, Davatzikos Christos, Paragios Nikos
Laboratoire MAS, Ecole Centrale de Paris, France.
Med Image Comput Comput Assist Interv. 2010;13(Pt 2):676-83. doi: 10.1007/978-3-642-15745-5_83.
In this paper, we introduce a novel approach to bridge the gap between the landmark-based and the iconic-based voxel-wise registration methods. The registration problem is formulated with the use of Markov Random Field theory resulting in a discrete objective function consisting of thee parts. The first part of the energy accounts for the iconic-based volumetric registration problem while the second one for establishing geometrically meaningful correspondences by optimizing over a set of automatically generated mutually salient candidate pairs of points. The last part of the energy penalizes locally the difference between the dense deformation field due to the iconic-based registration and the implied displacements due to the obtained correspondences. Promising results in real MR brain data demonstrate the potentials of our approach.
在本文中，我们介绍了一种新颖的方法，以弥合基于地标和基于图像的体素级配准方法之间的差距。配准问题是利用马尔可夫随机场理论来构建的，从而产生一个由三部分组成的离散目标函数。能量的第一部分考虑基于图像的体积配准问题，而第二部分则通过对一组自动生成的相互显著的候选点对进行优化来建立具有几何意义的对应关系。能量的最后一部分局部惩罚基于图像配准产生的密集变形场与由获得的对应关系所隐含的位移之间的差异。在真实的磁共振脑数据上取得的有前景的结果证明了我们方法的潜力。
