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Mutifractals based multimodal 3D image registration

delete2019-01-01
delete11
PRE
AI
N
N. Sivakumaran
DOI：10.1016/j.bspc.2018.08.015delete
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摘要

摘要

En 中文
多模态配准是一种配准不同模态 (例如，计算机断层摄影 (CT) 和磁共振 (MR)) 的体积的方法。基于互信息 (MI) 的方法被广泛用于多模态配准。MI表征体积的体素强度之间的统计依赖性。当在体积的体素强度之间存在低对应性时，基于MI的配准的鲁棒性受到影响。这可以通过将体积的几何特征 (如复杂性、奇异性和不规则性) 与配准相结合来改进。本文提出了一种基于体积多重分形特征的三维多模态图像配准方法。所提出的方法使用多重分形形式主义将几何特征纳入配准。多重分形形式主义涉及确定Holder指数，然后计算Hausdorff维数。Holder指数量化了体积的局部规律性，Hausdorff维度量化了体积的全局规律性 (多重分形)。使用针对不同噪声水平的合成体模图像和来自公共领域数据库的7个不同患者的41个临床3D脑图像来评估所提出算法的性能。上述测试平台突出了所提出的方法在提高配准的鲁棒性和准确性方面的效率。（C）2018爱思唯尔有限公司。版权所有。
Keyword:
Multifractal
Multimodal image registration
Mutual information
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期刊

Biomedical Signal Processing and Control 封面图
Biomedical Signal Processing and Control
IF:
4.9
论文数:
1.0W
被引数:
2.4W

机构

N
national institute of technology (nit system)
学者数:
4.0W
论文数: 3.7W
被引数: 31
National Institute of Technology Tiruchirappalli 封面图
National Institute of Technology Tiruchirappalli
学者数:
1.6K
论文数: 1.7K
被引数: 3.4K
引用论文

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