A robust hybrid method for nonrigid image registration

Authors:
Jinzhong Yang;James P. Williams;Yiyong Sun;Rick S. Blum;Chenyang Xu
Affiliations:
Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX 77030, USA;Angiography and Interventional Xray Division, Siemens Healthcare, Siemensstr. 1, Forchheim, 91301, Germany;Shanghai Microport EP MedTech Co., Ltd., Shanghai 201203, PR China;ECE Department, Lehigh University, Bethlehem, PA 18015, USA;Siemens Technology to Business Center, Berkeley, CA 94704, USA
Venue:
Pattern Recognition
Year:
2011

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Abstract

A nonrigid registration method is proposed to automatically align two images by registering two sets of sparse features extracted from the images. Motivated by the paradigm of Robust Point Matching (RPM) algorithms [1,2], which were originally proposed for shape registration, we develop Robust Hybrid Image Matching (RHIM) algorithm by alternatively optimizing feature correspondence and spatial transformation for image registration. Our RHIM algorithm is built to be robust to feature extraction errors. A novel dynamic outlier rejection approach is described for removing outliers and a local refinement technique is applied to correct non-exactly matched correspondences arising from image noise and deformations. Experimental results demonstrate the robustness and accuracy of our method.