基于互信息最大化的医学图像多模态非刚性配准

E. Ardizzone, O. Gambino, M. Cascia, Liliana Lo Presti, R. Pirrone
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引用次数: 15

摘要

提出了一种新的医学图像多模态非刚性配准技术。首先概述了配准问题,介绍了几种常用的配准方法,然后给出了该算法。该方法基于互信息最大化,通过适当的全局光滑仿射分段变换计算变形场。该算法在构思时特别注意计算量和结果的准确性。本文报道了脑CT和MR (T1、T2和PD模式)的患者内、患者间和脑图谱图像的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-modal non-rigid registration of medical images based on mutual information maximization
In this paper, a new multi-modal non-rigid registration technique for medical images is presented. Firstly, the registration problem is outlined and some of the most common approaches reported, then, the proposed algorithm is presented. The proposed technique is based on mutual information maximization and computes a deformation field through a suitable globally smoothed affine piecewise transformation. The algorithm has been conceived with particular attention to computational load and accuracy of results. Experimental results involving intra-patient, inter-patients and atlas images on brain CT and MR (T1, T2 and PD modalities) are reported.
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