Nonrigid Registration of Volumetric Images Using Ranked Order Statistics
Nonrigid Registration of Volumetric Images Using Ranked Order Statistics
复制标题
使用排序统计的体积图像的非刚性配准
DOI：
10.1109/tmi.2013.2286192
复制
发表时间：
2014
影响因子：
10.6
通讯作者：
Marleen de Bruijne
中科院分区：
文献类型：
--
作者：
Ruwan Tennakoon;A. Bab;Z. Cao;Marleen de Bruijne
基于灰度相似性测度的非刚性图像配准技术广泛应用于医学成像领域。由于这些技术的高计算复杂性，特别是对于体积图像，找到合适的配准方法，以减少计算负担和增加配准精度已成为一个密集的研究领域。在本文中，我们提出了一种快速，准确的非刚性配准方法内模态体积图像。我们的方法利用了顺序统计的分割方法提供的信息，找到重要的区域进行注册，并使用适当的采样方案，以针对这些地区，减少注册计算时间。所提出的方法的一个独特的优点是它能够识别收益递减点和停止注册过程。我们的实验上登记的吸气末呼气末肺CT扫描对，与专家注释的地标，表明新的方法是更快，更准确的比国家的最先进的采样为基础的技术，特别是注册的图像与大变形。
Nonrigid image registration techniques using intensity based similarity measures are widely used in medical imaging applications. Due to high computational complexities of these techniques, particularly for volumetric images, finding appropriate registration methods to both reduce the computation burden and increase the registration accuracy has become an intensive area of research. In this paper, we propose a fast and accurate nonrigid registration method for intra-modality volumetric images. Our approach exploits the information provided by an order statistics based segmentation method, to find the important regions for registration and use an appropriate sampling scheme to target those areas and reduce the registration computation time. A unique advantage of the proposed method is its ability to identify the point of diminishing returns and stop the registration process. Our experiments on registration of end-inhale to end-exhale lung CT scan pairs, with expert annotated landmarks, show that the new method is both faster and more accurate than the state of the art sampling based techniques, particularly for registration of images with large deformations.
影响因子：
3.8
作者：
Coselmon, MM;Balter, JM;Kessler, ML
通讯作者：
Kessler, ML