Medical Image Alignment by Normal Vector Information

In this paper, a new approach on image registration is presented. We introduce a novel conception- normal vector information (NVI) – to evaluate the similarity between two images. NVI method takes advantage of the relationship between voxels in the image to extract the normal vector (NV) information of each voxel. Firstly, NVI criterion is presented. Then, based on the criterion, we find that NVI related metric has a quite perfect global optimal value on transformation parameter ranges. Finally, registration examples which are based on NVI criterion are provided. The result implies that the feature of smooth value distribution and one global optimal value that NVI metric has makes the optimization procedure much easier to be implemented in image registration.

[1]  R. Bernstein,et al.  Shading 3D-Images from CT Using Gray-Level Gradients , 1986, IEEE Transactions on Medical Imaging.

[2]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[3]  David R. Haynor,et al.  Nonrigid multimodality image registration , 2001, SPIE Medical Imaging.

[4]  Hongen Liao,et al.  Medical Imaging and Augmented Reality , 2004 .

[5]  Ghassan Hamarneh,et al.  MATLAB-ITK interface for medical image filtering, segmentation, and registration , 2006, SPIE Medical Imaging.

[6]  Michael Unser,et al.  Optimization of mutual information for multiresolution image registration , 2000, IEEE Trans. Image Process..

[7]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[8]  Lydia Ng,et al.  Medical lmage Registration: Concepts and Implementation , 2004 .

[9]  Jianfeng Xu,et al.  Evaluation of Morphological Reconstruction, Fast Marching and a Novel Hybrid Segmentation Method , 2004, CIS.

[10]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[11]  Milan Sonka,et al.  Medical Imaging 2001: Image Processing , 2001 .

[12]  Yuxi Fu,et al.  Computational and Information Science, First International Symposium, CIS 2004, Shanghai, China, December 16-18, 2004, Proceedings , 2004, CIS.

[13]  Lixu Gu,et al.  Dynamic Heart Modeling Based on a Hybrid 3D Segmentation Approach , 2004, MIAR.