基于算术调和均值距离测度的弹性图像配准

针对图像配准中的特征提取问题,提出了一种基于自动选取标记点的弹性图像配准方法。首先,提出了新的相似性测度,算术-调和均值距离测度AHM,图像通过最大化AHM全局配准,然后将图像划分成均匀子块,采用AHM测度刚性配准各对应子块,选取对应子块的中心作为标记点对,最后,用三次均匀B样条实现图像的弹性配准。实验结果表明,AHM是有效的,其配准速度是互信息的2倍。

Arithmetic harmonic mean divergence measure for elastic image registration

To solve the problem of feature extraction in image registration,an automatic method to extract landmark points for elastic medical image registration is proposed.First,a new similarity measure,named as arithmetic harmonic mean divergence measure(AHM),is induced.The reference image and floating image are rigidly registered by AHM measure.Then,the two images are divided into uniform blocks.The landmarks in object are extracted by regional rigid registration by maximizing AHM measure between the corresponding subimages.According to corresponding landmarks,the global elastic registration is achieved by bicubic B-spline functions.Experiments on registration of brain Magnetic Resonance(MR) images and Computed Tomography(CT) images show that the proposed method is accurate and fast.