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Information-Theoretic Multi-modal Image Registration Based on the Improved Fast Gauss Transform: Application to Brain Images

机译：基于改进的快速高斯变换的信息理论多模态图像配准：在脑图像中的应用

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摘要

Performances of multi-modality image registration methods that are based on information-theoretic registration criteria crucially depend on the specific computational implementation. We proposed a new implementation based on the improved fast Gauss transform so as to estimate, from all available intensity samples, the intensity density functions needed to compute the information-theoretic criteria. The proposed and several other state-of-the-art implementations were tested and compared in 3-D rigid-body registration of multi-modal brain volumes. Experimental results indicate that the proposed implementation achieves the most consistent spatial alignment of brain volumes at a subpixel accuracy.

机译：基于信息理论配准标准的多模态图像配准方法的性能关键取决于特定的计算实现。我们提出了一种基于改进的快速高斯变换的新实现，以便从所有可用的强度样本中估算计算信息理论标准所需的强度密度函数。在多模态大脑体积的3-D刚体配准中测试并比较了建议的和其他几种最新的实现方式。实验结果表明，所提出的实现以亚像素精度实现了最一致的大脑体积空间对齐。

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来源
《Multimodal Brain Image Analysis》|2011年|p.135-142|共8页
会议地点 Toronto(CA);Toronto(CA);Toronto(CA);Toronto(CA)
作者
Ziga Spiclin; Bostjan Likar; Pranjo Pernus;
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作者单位

Faculty of Electrical Engineering, Laboratory of Imaging Technologies,University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia;

Faculty of Electrical Engineering, Laboratory of Imaging Technologies,University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia;

Faculty of Electrical Engineering, Laboratory of Imaging Technologies,University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia;

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原文格式 PDF
正文语种 eng
中图分类医用物理学;脑部疾病;
关键词
image registration; multi-modality; gauss transform; joint entropy; mutual information; brain;

机译：图像配准；多模式高斯变换联合熵相互信息；脑;

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专利

1. Improved Gauss-Newton optimisation methods in affine registration of SPECT brain images [J] . D. Salas-Gonzalez, J.M. Gorriz, J. Ramirez, Electronicsletters . 2008,第22期

机译：改进的Gauss-Newton优化方法在SPECT脑图像的仿射配准中
2. Improved Gauss-Newton optimisation methods in affine registration of SPECT brain images [J] . Salas-Gonzalez D., Gorriz J.M., Ramirez J., Electronics Letters . 2008,第22期

机译：改进的Gauss-Newton优化方法在SPECT脑图像的仿射配准中
3. Performance analysis of brain image registration technique using fast walsh hadamard transform and modified adaptive polar transform [J] . D Sasikala, R Neelaveni Journal of Scientific & Industrial Research . 2011,第2期

机译：快速沃尔什哈达玛德变换和改进的自适应极坐标变换的脑图像配准技术性能分析
4. Information-Theoretic Multi-modal Image Registration Based on the Improved Fast Gauss Transform: Application to Brain Images [C] . Ziga Spiclin, Bostjan Likar, Franjo Pernus International Workshop on Multimodal Brain Image Analysis . 2011

机译：基于改进的快速高斯变换的信息 - 理论多模态图像配准：应用于脑图像
5. Variational and spline based multi-modal non-rigid medical image registration and applications [D] . Zhang, Zhijun 2006

机译：基于变分和样条的多模态非刚性医学图像配准及应用
6. Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images [O] . Simona Moldovanu, Lenuta Pană Toporaș, Anjan Biswas, 2020

机译：结合稀疏和密集功能以改善脑DTI图像的多模态注册
7. Development of Efficient Intensity Based Registration Techniques for Multi-modal Brain Images [O] . Pradhan Smita 2016

机译：基于有效强度的多模式脑图像配准技术的发展

Information-Theoretic Multi-modal Image Registration Based on the Improved Fast Gauss Transform: Application to Brain Images

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