This paper proposes a novel elastic model and presents a deformable registration method based on the model. The method registers images without the need to extract features from the images, and therefore works directly on grey-level images. A new similarity metric is given on which the formation of external forces is based. The registration method, taking the coarse-to-fine strategy, constructs external forces in larger scales for the first few iterations to rely more on global evidence, and then in smaller scales for later iterations to allow local refinements. The stiffness of the elastic body decreases as the process proceeds.
To make it widely applicable, the method is not restricted to any type of transformation. The variations between images are thought as general free-form deformations. Because the elastic model designed is linearized, it can be solved very efficiently with high accuracy.
The method has been successfully tested on MRI images. It will certainly find other uses such as matching time-varying sequences of pictures for motion analysis, fitting templates into images for non-rigid object recognition, matching stereo images for shape recovery, etc.
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This work was done at the National Laboratory of Pattern Recognition, China.
Guan Weiguang is an Associate Professor of the Computer Science Department, Harbin University of Science and Technology, and a guest researcher at National Laboratory of Pattern Recognition (NLPR). He received doctoral degree in pattern recognition and intelligent control from NLPR in 1995. His research interests include computer vision, volume visualization, virtual reality, and image processing.
Xie Lin is an Engineer of the First Hospital of Harbin Medical University. Her research interests include medical image processing and analysis, and database.
Ma Songde is a Professor at National Laboratory of Pattern Recognition, an IEEE senior member. He received his Ph.D. degree in computer vision from the University of Paris VI, France in 1986. His research interests include computer vision, robotics, image processing, computer graphics, and multimedia.
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Weiguang, G., Lin, X. & Songde, M. Deformable registration of digital images. J. of Comput. Sci. & Technol. 13, 246 (1998). https://doi.org/10.1007/BF02943193
- Image registration
- deformable model
- deformable registration
- local similarity function