基于调幅-调频与熵图多模态医学图像的配准

背景：基于传统互信息量的多模态医学图像配准方法配准时需要利用二维直方图或者Parzen窗函数的方法估计概率密度分布,进而计算互信息量,这种方式计算速度慢,而且只考虑了图像的灰度信息,容易出现误配。目的：针对目前主流的配准方法鲁棒性差、耗时的缺点,提出了一种新的基于调幅-调频（AM-FM）特征互信息量的快速配准方法。方法：该方法考虑了图像的空间和结构信息;首先通过AM-FM模型对图像进行分解,得到图像的AM-FM特征,与图像的灰度特征一起组成高维特征;然后利用熵图和最小生成树加快AM-FM特征互信息量的计算,从而实现了医学图像的快速配准。结果与结论：对20组磁共振T1-T2加权图像、CT/正电子发射计算机断层成像图像进行了实验,结果表明该方法在图像空间分辨率较低,有噪声影响等情况下均可以达到较好的结果,且配准精度优于国际上的主流方法,具有计算速度快,精度高,鲁棒性强的特点,适于临床应用。

Multi-Modality Medical Image Registration Using AM-FM and Entropy Graph

BACKGROUND：Traditional mutual information（MI） based multimodal medical image registration evaluates probability density function by two-dimensional histogram or Parzen-window function,and then plug this estimation into the expression of MI.In this process,it just considers images intensity,but ignore spatial information of image,which leading to image mismatch.OBJECTIVE：To propose a new robust and fast registration method based on amplitude modulation-frequency modulation（AM-FM） .METHODS：Firstly,we decomposed the image using AM-FM model so as to obtain the AM-FM features.Therefore,we got a series of high dimension features consisting of AM-FM features and intensity features.Finally,we computed mutual information using these high dimension features and entropy graph.It was an extension to the mutual information framework which incorporated spatial information about image structure into the registration process and had the potential to improve the accuracy and robustness of image registration,entropy graph was used to compute the mutual information.RESULTS AND CONCLUSION：In the experiment,we used 20 sets of data：T1-T2 weighted images and CT-PET images to compare the proposed method with MI.The results indicate that this algorithm is a more robust for image registration than conditional mutual information,even if the image in the context of low resolution and noise.This method is more accurate and robust while remaining computationally efficient.Hence,it is particularly suited to the clinical application.