Tracking using motion estimation with physically motivated inter-region constraints
KAUST DepartmentElectrical Engineering Program
Applied Mathematics and Computational Science Program
Visual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Preprint Posting Date2014-02-06
Permanent link to this recordhttp://hdl.handle.net/10754/563740
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AbstractWe 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.
CitationArif, 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.2014.2325040
SponsorsThis 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.