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使用广义生存指数熵的多模态图像配准

Multi-modal image registration using the generalized survival exponential entropy.

作者信息

Liao Shu, Chung Albert C S

机构信息

Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong.

出版信息

Med Image Comput Comput Assist Interv. 2006;9(Pt 2):964-71. doi: 10.1007/11866763_118.

Abstract

This paper introduces a new similarity measure for multimodal image registration task. The measure is based on the generalized survival exponential entropy (GSEE) and mutual information (GSEE-MI). Since GSEE is estimated from the cumulative distribution function instead of the density function, it is observed that the interpolation artifact is reduced. The method has been tested on four real MR-CT data sets. The experimental results show that the GSEE-MI-based method is more robust than the conventional MI-based method. The accuracy is comparable for both methods.

摘要

本文介绍了一种用于多模态图像配准任务的新相似性度量。该度量基于广义生存指数熵(GSEE)和互信息(GSEE-MI)。由于GSEE是从累积分布函数而非密度函数估计得到的,因此可以观察到插值伪影有所减少。该方法已在四个真实的磁共振成像-计算机断层扫描(MR-CT)数据集上进行了测试。实验结果表明,基于GSEE-MI的方法比传统的基于互信息的方法更稳健。两种方法的准确性相当。

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