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使用图形匹配三维俯卧位和仰卧位CT结肠造影扫描图像。

Matching 3-D prone and supine CT colonography scans using graphs.

作者信息

Wang Shijun, Petrick Nicholas, Van Uitert Robert L, Periaswamy Senthil, Wei Zhuoshi, Summers Ronald M

机构信息

Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892-1182, USA.

出版信息

IEEE Trans Inf Technol Biomed. 2012 Jul;16(4):676-82. doi: 10.1109/TITB.2012.2194297. Epub 2012 Apr 27.

Abstract

In this paper, we propose a new registration method for prone and supine computed tomographic colonography scans using graph matching. We formulate 3-D colon registration as a graph matching problem and propose a new graph matching algorithm based on mean field theory. In the proposed algorithm, we solve the matching problem in an iterative way. In each step, we use mean field theory to find the matched pair of nodes with highest probability. During iterative optimization, one-to-one matching constraints are added to the system in a step-by-step approach. Prominent matching pairs found in previous iterations are used to guide subsequent mean field calculations. The proposed method was found to have the best performance with smallest standard deviation compared with two other baseline algorithms called the normalized distance along the colon centerline (NDACC) ( p = 0.17) with manual colon centerline correction and spectral matching ( p < 1e-5). A major advantage of the proposed method is that it is fully automatic and does not require defining a colon centerline for registration. For the latter NDACC method, user interaction is almost always needed for identifying the colon centerlines.

摘要

在本文中,我们提出了一种使用图匹配的俯卧位和仰卧位计算机断层结肠造影扫描的新配准方法。我们将三维结肠配准公式化为一个图匹配问题,并基于平均场理论提出了一种新的图匹配算法。在所提出的算法中,我们以迭代方式解决匹配问题。在每一步中,我们使用平均场理论找到具有最高概率的匹配节点对。在迭代优化过程中,一对一匹配约束以逐步方式添加到系统中。在先前迭代中找到的突出匹配对用于指导后续的平均场计算。与另外两种基线算法(称为沿结肠中心线的归一化距离(NDACC)(p = 0.17),需手动校正结肠中心线)和光谱匹配(p < 1e - 5)相比,所提出的方法被发现具有最小标准差的最佳性能。所提出方法的一个主要优点是它是完全自动的,并且不需要为配准定义结肠中心线。对于后一种NDACC方法,几乎总是需要用户交互来识别结肠中心线。

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本文引用的文献

2
Registration under topological change for CT colonography.
IEEE Trans Biomed Eng. 2011 May;58(5):1403-11. doi: 10.1109/TBME.2011.2105267. Epub 2011 Jan 10.
3
Supine and prone colon registration using quasi-conformal mapping.
IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1348-57. doi: 10.1109/TVCG.2010.200.
4
Combining Statistical and Geometric Features for Colonic Polyp Detection in CTC Based on Multiple Kernel Learning.
Int J Comput Intell Appl. 2010 Jan 1;9(1):1-15. doi: 10.1142/S1469026810002744.
5
Effect of computer-aided detection for CT colonography in a multireader, multicase trial.
Radiology. 2010 Sep;256(3):827-35. doi: 10.1148/radiol.10091890. Epub 2010 Jul 27.
6
Cancer statistics, 2010.
CA Cancer J Clin. 2010 Sep-Oct;60(5):277-300. doi: 10.3322/caac.20073. Epub 2010 Jul 7.

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