Tracking using motion estimation with physically motivated inter-region constraints
Type
ArticleKAUST Department
Electrical Engineering ProgramApplied Mathematics and Computational Science Program
Visual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2014-09Preprint Posting Date
2014-02-06Permanent link to this record
http://hdl.handle.net/10754/563740
Metadata
Show full item recordAbstract
We propose a method for tracking structures (e.g., ventricles and myocardium) in cardiac images (e.g., magnetic resonance) by propagating forward in time a previous estimate of the structures using a new physically motivated motion estimation scheme. Our method estimates motion by regularizing only within structures so that differing motions among different structures are not mixed. It simultaneously satisfies the physical constraints at the interface between a fluid and a medium that the normal component of the fluid's motion must match the normal component of the medium's motion and the No-Slip condition, which states that the tangential velocity approaches zero near the interface. We show that these conditions lead to partial differential equations with Robin boundary conditions at the interface, which couple the motion between structures. We show that propagating a segmentation across frames using our motion estimation scheme leads to more accurate segmentation than traditional motion estimation that does not use physical constraints. Our method is suited to interactive segmentation, prominently used in commercial applications for cardiac analysis, where segmentation propagation is used to predict a segmentation in the next frame. We show that our method leads to more accurate predictions than a popular and recent interactive method used in cardiac segmentation. © 2014 IEEE.Citation
Arif, O., Sundaramoorthi, G., Hong, B.-W., & Yezzi, A. (2014). Tracking Using Motion Estimation With Physically Motivated Inter-Region Constraints. IEEE Transactions on Medical Imaging, 33(9), 1875–1889. doi:10.1109/tmi.2014.2325040Sponsors
This work was supported in part by KAUST Baseline and Visual Computing Center funding, Korea NRF-2010-220-D00078 and NRF-2011-0007898, and the National Science Foundation (NSF) under Grant CCF-1347191 and Grant CMMI-1068624.arXiv
1402.1503Additional Links
http://arxiv.org/abs/arXiv:1402.1503v1ae974a485f413a2113503eed53cd6c53
10.1109/TMI.2014.2325040