Automatic registration between 3D intra-operative ultrasound and pre-operative CT images of the liver based on robust edge matching
Automatic registration between 3D intra-operative ultrasound and pre-operative CT images of the liver based on robust edge matching
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DOI：
10.1088/0031-9155/57/1/69
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发表时间：
2012-01-07
影响因子：
3.5
通讯作者：
Ra, Jong Beom
中科院分区：
文献类型：
--
作者：
Nam, Woo Hyun;Kang, Dong-Goo;Ra, Jong Beom
三维（3D）超声（US）图像与计算机断层摄影（CT）或磁共振图像的配准在各种临床应用中是有益的，例如肝脏的诊断和图像引导介入。然而，传统的方法通常需要一个耗时和不方便的手动预对准过程，这个过程的成功很大程度上取决于初始变换参数的正确选择。在本文中，我们提出了一个自动的基于特征的仿射配准过程的三维手术中的US和术前CT图像的肝脏。在配准过程中，我们首先从3D B型超声图像分割血管腔和肝脏表面。然后，我们自动估计的初始注册转换，通过使用所提出的边缘匹配算法。该算法基于改进的Viterbi算法以非迭代方式找到两幅图像的血管中心线之间最可能的对应关系。最后，通过联合使用血管和肝脏表面信息，在全局仿射变换的基础上迭代地改进配准。通过定性和定量评价，在合成数据集和20个临床数据集上对所提出的配准算法进行了验证。实验结果表明，即使在初始错位较大的情况下，该方法也能成功地实现三维B超图像与CT图像的自动配准。
The registration of a three-dimensional (3D) ultrasound (US) image with a computed tomography (CT) or magnetic resonance image is beneficial in various clinical applications such as diagnosis and image-guided intervention of the liver. However, conventional methods usually require a time-consuming and inconvenient manual process for pre-alignment, and the success of this process strongly depends on the proper selection of initial transformation parameters. In this paper, we present an automatic feature-based affine registration procedure of 3D intra-operative US and pre-operative CT images of the liver. In the registration procedure, we first segment vessel lumens and the liver surface from a 3D B-mode US image. We then automatically estimate an initial registration transformation by using the proposed edge matching algorithm. The algorithm finds the most likely correspondences between the vessel centerlines of both images in a non-iterative manner based on a modified Viterbi algorithm. Finally, the registration is iteratively refined on the basis of the global affine transformation by jointly using the vessel and liver surface information. The proposed registration algorithm is validated on synthesized datasets and 20 clinical datasets, through both qualitative and quantitative evaluations. Experimental results show that automatic registration can be successfully achieved between 3D B-mode US and CT images even with a large initial misalignment.