Authors:
Gustaf Johansson
;
Mats Andersson
and
Hans Knutsson
Affiliation:
Linköping University, Sweden
Keyword(s):
Image Registration, Missing Data, Medical Image Processing, Global Linear Optimization.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Computer Vision, Visualization and Computer Graphics
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Imaging
;
Methodologies and Methods
;
Missing Data
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Signal Processing
;
Software Engineering
;
Theory and Methods
Abstract:
In image registration it is often necessary to employ regularization in one form or another to be able to find
a plausible displacement field. In medical applications, it is useful to define different constraints for different
areas of the data. For instance to measure if organs have moved as expected after a finished treatment. One
common problem is how to find plausible motion vectors far away from known motion. This paper introduces
a new method to build and solve a Global Linear Optimizations (GLO) problem with a novel set of terms
which enable specification of border areas to allow a sliding motion. The GLO approach is important especially
because it allows simultaneous incorporation of several different constraints using information from
medical atlases such as localization and properties of organs. The power and validity of the method is demonstrated
using two simple, but relevant 2D test images. Conceptual comparisons with previous methods are
also made to highlight the contr
ibutions made in this paper. The discussion explains important future work
and experiments as well as exciting future improvements to the GLO framework.
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