Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

Stochastic Image Registration with User Constraints

Institution:
1Georgia Institute of Technology, Atlanta, GA, USA.
2Comprehensive Cancer Center/ECE, UAB, AL, USA.
Publication Date:
Mar-2013
Journal:
Proc Soc Photo Opt Instrum Eng
Volume Number:
8669
Citation:
Proc Soc Photo Opt Instrum Eng. 2013 Mar 13;8669.
PubMed ID:
24357915
PMCID:
PMC3865237
Keywords:
Non-rigid, Registration, constraint, user, particle filter, implicit regularization, tochastic optimization
Appears in Collections:
NAC, NA-MIC, NCIGT
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
P41 RR013642/RR/NCRR NIH HHS/United States
U41 RR019703/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Kolesov I., Lee J., Vela P., Tannenbaum A. Stochastic Image Registration with User Constraints. Proc Soc Photo Opt Instrum Eng. 2013 Mar 13;8669. PMID: 24357915. PMCID: PMC3865237.
Downloaded: 909 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

Constrained registration is an active area of research and is the focus of this work. This note describes a non-rigid image registration framework for incorporating landmark constraints. Points that must remain stationary are selected, the user chooses the spatial extent of the inputs, and an automatic step computes the deformable registration, respecting the constraints. Parametrization of the deformation field is by an additive composition of a similarity transformation and a set of Gaussian radial basis functions. The bases' centers, variances, and weights are determined with a global optimization approach that is introduced. This approach is based on the particle filter for performing constrained optimization; it explores a series of states defining a deformation field that is physically meaningful (i.e., invertible) and prevents chosen points from moving. Results on synthetic two dimensional images are presented.

Additional Material
1 File (190.31kB)
Kolesov-ProcSocPhotoOptInstrumEng2013-fig3.jpg (190.31kB)