Feature-aligned 4D spatiotemporal image registration

Huanhuan Xu, Peizhi Chen, Wuyi Yu, Amit Sawant, S. S. Iyengar, Xin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


In this paper, we develop a feature-aware 4D spatiotemporal image registration method. Our model is based on a 4D (3D+time) free-form B-spline deformation model which has both spatial and temporal smoothness. We first introduce an automatic 3D feature extraction and matching method based on an improved 3D SIFT descriptor, which is scale- and rotation- invariant. Then we use the results of feature correspondence to guide an intensity-based deformable image registration. Experimental results show that our method can lead to smooth temporal registration with good matching accuracy; therefore this registration model is potentially suitable for dynamic tumor tracking.

Original languageEnglish (US)
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Number of pages4
StatePublished - 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: Nov 11 2012Nov 15 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other21st International Conference on Pattern Recognition, ICPR 2012

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Feature-aligned 4D spatiotemporal image registration'. Together they form a unique fingerprint.

Cite this