7,194 research outputs found
06311 Abstracts Collection -- Sensor Data and Information Fusion in Computer Vision and Medicine
From 30.07.06 to 04.08.06, the Dagstuhl Seminar 06311 ``Sensor Data and Information Fusion in Computer Vision and Medicine\u27\u27 was held
in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
Sensor data fusion is of increasing importance for many
research fields and applications. Multi-modal imaging
is routine in medicine, and in robitics it is
common to use multi-sensor data fusion.
During the seminar, researchers and application experts
working in the field of sensor data
fusion presented their current
research, and ongoing work and open problems were discussed.
Abstracts of the presentations given during
the seminar as well as abstracts of seminar
results and ideas are put together in this paper.
The first section describes the seminar topics and goals in general.
The second part briefly summarizes the contributions
A Simple Regularizer for B-spline Nonrigid Image Registration That Encourages Local Invertibility
Nonrigid image registration is an important task for many medical imaging applications. In particular, for radiation oncology it is desirable to track respiratory motion for thoracic cancer treatment. B-splines are convenient for modeling nonrigid deformations, but ensuring invertibility can be a challenge. This paper describes sufficient conditions for local invertibility of deformations based on B-spline bases. These sufficient conditions can be used with constrained optimization to enforce local invertibility. We also incorporate these conditions into nonrigid image registration methods based on a simple penalty approach that encourages diffeomorphic deformations. Traditional Jacobian penalty methods penalize negative Jacobian determinant values only at grid points. In contrast, our new method enforces a sufficient condition for invertibility directly on the deformation coefficients to encourage invertibility globally over a 3-D continuous domain. The proposed penalty approach requires substantially less compute time than Jacobian penalties per iteration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85951/1/Fessler21.pd
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