多模态医学图象的SVD-ICP配准方法

多模态医学图象的配准在医学诊断和治疗计划中起着重要的作用。本文提出一种基于轮廓特征的迭代最近点（ＳＶＤ－ＩＣＰ）的配准方法。这种方法结合了ＳＶＤ最优化解析方法和迭代搜索的优点来解决图象轮廓点的匹配问题，适用于不同模态医学图象之间的配准。我们关于ＣＴ－ＭＲＩ和ＰＥＴ－ＭＲＩ二维图象的配准实验证明了该方法的有效性。

A New Method Based on Contour Feature for Multi-modality Medical Image Registration

Multi-modality medical image registration and fusion have important applications in clinical diagnosis and therapy planning. It is essential to accurately align two images from different modalities prior to any operation of fusion. This paper presents an SVD-ICP (Single Value Decomposition-Iterative Closest Points) method to register brain images based on contour feature, which combines the advantages of the speed of SVD analytical optimization and the precision of iterative search to solve the problem of image contour points matching. It uses feature sampling and accelerating algorithm to reduce computation time. The method to extract the contour is semiautomatic so that the accuracy and reliability are assured. It is applicable to multi-modality medical image registration, the original SVD-ICP algorithm is in fact an appropriate solution to the problem of n-Dimension space points matching. Our experiments on CT-MRI and PET-MRI registration prove that this method is effective.