Non-rigid registration using morphons

Authors:
Andreas Wrangsjö;Johanna Pettersson;Hans Knutsson
Affiliations:
Medical Informatics, Department of Biomedical Engineering, and Center for Medical Image Science and Visualization, Linköping University, Sweden;Medical Informatics, Department of Biomedical Engineering, and Center for Medical Image Science and Visualization, Linköping University, Sweden;Medical Informatics, Department of Biomedical Engineering, and Center for Medical Image Science and Visualization, Linköping University, Sweden
Venue:
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Year:
2005

Citing 4
Cited 2

Computation of component image velocity from local phase information

International Journal of Computer Vision
Performance of optical flow techniques

International Journal of Computer Vision
Signal Processing for Computer Vision

Signal Processing for Computer Vision
Disparity from Monogenic Phase

Proceedings of the 24th DAGM Symposium on Pattern Recognition

3D deformable registration for monitoring radiotherapy treatment in prostate cancer

SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Diffeomorphic registration of images with variable contrast enhancement

Journal of Biomedical Imaging - Special issue on modern mathematics in biomedical imaging

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Morphon, a non-rigid registration method is presented and applied to a number of registration applications. The algorithm takes a prototype image (or volume) and morphs it into a target image using an iterative, multi-resolution technique. The deformation process is done in three steps: displacement estimation, deformation field accumulation and deformation. The framework could be described in very general terms, but in this paper we focus on a specific implementation of the Morphon framework. The method can be employed in a wide range of registration tasks, which is shown in four very different registration examples; 2D photographs of hands and faces, 3D CT data of the hip region, and 3D MR brain images.