全局异常信号环境下基于体素灰度多模医学图像配准研究  

Research on Voxel Indensity Based Multi-modal Medical Image Registration When Gross Outliers Are Observed

作　　者：秦斌杰[1] 庄天戈[1] 

机构地区：[1]上海交通大学生物医学工程系,上海200030

出　　处：《航天医学与医学工程》2004年第2期140-143,共4页Space Medicine & Medical Engineering

基　　金：上海市科学发展基金资助项目 ( 985 10 70 16)

摘　　要：目的在全局异常信号环境下 ,找出适合于临床应用的、满足精度和鲁棒性要求的基于体素灰度多模医学图像配准相似性测度。方法结合对各种相似性测度的分析 ,对无异常信号的实际医学图像 ,和分别加了随机噪声及全局异常信号的多模医学图像进行配准精度的分析。结果对各种已有成熟的相似性测度进行理论分析和实验对比研究的基础上 ,归一化互信息在全局异常信号环境下对多模医学图像进行配准 ,它们的配准精度和鲁棒性表现都令人满意 ,能得到准确的配准结果。而基于相关比和互信息的配准方法 ,不能准确地配准加了全局异常信号的多模医学图像。结论相比于其他相似性测度 ,归一化互信息在全局异常信号环境下 ,是一个能满足配准精度和鲁棒性要求的合适相似性测度。Objective To find the most accurate and robust similarity measures for clinical use by comparison research on popular similarity measures for voxel intensity based multi-modal medical image registration, when gross outliers are observed in multi-modal medical images. Method Under theoretic analysis of different similarity measures used in registration of multi-modal medical images with gross outlier, using real multi-modal medical images without and with gross outliers and random noise signals, registration experiments were implemented to evaluate different performances of similarity measures for accuracy and robustness. Result It was found that normalized mutual information based registration method has special capability in getting accurate registration results when gross outliers are observed in multi-modal medical image registration, whereas correlation ratio and mutual information failed to get correct registration results. Conclusion Compared with other similarity measures, normalized mutual information was the most accurate and robust similarity measure for 3D multi-modal medical image registration when gross outliers are observed in images to be registered.

关 键 词：图像配准 全局异常信号 相似性测度 相关比 互信息 归一化互信息 

分 类 号：R319[医药卫生—基础医学]