Evaluation of Medical Image Registration by Using 3D SIFT and Phase-Only Correlation

An effective method for quantitatively evaluating rigid and non-rigid image registration without any manual assessment is proposed. This evaluation method is based on feature point detection in reference images and corresponding point localization in registered floating images. For feature point detection, a 3D SIFT keypoint detector is applied to determine evaluation reference points in liver vessel regions of reference images. For corresponding point localization, a 3D phase-only correlation approach is applied to match reference points and their corresponding points. Distance between the reference points and the correspondences can be used to estimate image registration errors. With the proposed method, users can evaluate different registration algorithms using their own image data automatically.

[1]  Li Zisheng,et al.  Efficient Rigid Registration for Medical Images Based on Small Sample Set , 2011 .

[2]  Ghassan Hamarneh,et al.  n -SIFT: n -Dimensional Scale Invariant Feature Transform , 2009, IEEE Trans. Image Process..

[3]  Gerald Q. Maguire,et al.  Comparison and evaluation of retrospective intermodality brain image registration techniques. , 1997, Journal of computer assisted tomography.

[4]  Bernd Fischer,et al.  Biomedical Image Registration, 4th International Workshop, WBIR 2010, Lübeck, Germany, July 11-13, 2010. Proceedings , 2010, Workshop on Biomedical Image Registration.

[5]  Imran A. Pirwani,et al.  Introduction to the Non-rigid Image Registration Evaluation Project (NIREP) , 2006, WBIR.

[6]  D. Hill,et al.  Medical image registration , 2001, Physics in medicine and biology.

[7]  T. Higuchi,et al.  A Sub-Pixel Correspondence Search Technique for Computer Vision Applications , 2004 .

[8]  Carl-Fredrik Westin,et al.  Tissue Classification Based on 3D Local Intensity Structures for Volume Rendering , 2000, IEEE Trans. Vis. Comput. Graph..

[9]  Pierre Hellier,et al.  Level Set Methods in an EM Framework for Shape Classification and Estimation , 2004, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[10]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[11]  Luc Soler,et al.  Portal Vein Registration for the Follow-Up of Hepatic Tumours , 2004, MICCAI.

[12]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.