Constrained non-rigid registration for whole body image registration: method and validation

2007 ◽  
Author(s):  
Xia Li ◽  
Thomas E. Yankeelov ◽  
Todd E. Peterson ◽  
John C. Gore ◽  
Benoit M. Dawant
Keyword(s):  
Body Image ◽  
Image Registration ◽  
Whole Body ◽  
Registration Method ◽  
Rigid Registration

Patient-Specific Meshless Model for Whole-Body Image Registration

2014 ◽  
pp. 50-57 ◽  
Author(s):  
Mao Li ◽  
Karol Miller ◽  
Grand Joldes ◽  
Ron Kikinis ◽  
Adam Wittek
Keyword(s):  
Body Image ◽  
Image Registration ◽  
Whole Body ◽  
Patient Specific

An accurate 3D shape context based non-rigid registration method for mouse whole-body skeleton registration

2011 ◽  
Author(s):  
Di Xiao ◽  
David Zahra ◽  
Pierrick Bourgeat ◽  
Paula Berghofer ◽  
Oscar Acosta Tamayo ◽  
...  
Keyword(s):  
Whole Body ◽  
Shape Context ◽  
Registration Method ◽  
Rigid Registration ◽  
3D Shape

scholarly journals A non-rigid registration method for mouse whole body skeleton registration

2010 ◽  
Author(s):  
Di Xiao ◽  
David Zahra ◽  
Pierrick Bourgeat ◽  
Paula Berghofer ◽  
Oscar Acosta Tamayo ◽  
...  
Keyword(s):  
Whole Body ◽  
Registration Method ◽  
Rigid Registration

scholarly journals Biomechanical model for computing deformations for whole-body image registration: A meshless approach

2016 ◽  
Vol 32 (12) ◽  
pp. e02771 ◽  
Author(s):  
Mao Li ◽  
Karol Miller ◽  
Grand Roman Joldes ◽  
Ron Kikinis ◽  
Adam Wittek
Keyword(s):  
Body Image ◽  
Image Registration ◽  
Biomechanical Model ◽  
Whole Body

scholarly journals An Image Registration Method for Colposcopic Images

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Efrén Mezura-Montes ◽  
Héctor-Gabriel Acosta-Mesa ◽  
Darío-del-Sinaí Ramírez-Garcés ◽  
Nicandro Cruz-Ramírez ◽  
Rodolfo Hernández-Jiménez
Keyword(s):  
Time Series ◽  
Body Image ◽  
Image Registration ◽  
Polynomial Approximation ◽  
Precancerous Lesions ◽  
Window Size ◽  
Image Sequences ◽  
Reference Image ◽  
Search Process ◽  
Registration Method

A nonrigid body image registration method for spatiotemporal alignment of image sequences obtained from colposcopy examinations to detect precancerous lesions of the cervix is proposed in this paper. The approach is based on time series calculation for those pixels in the first image of the sequence and a division of such image into small windows. A search process is then carried out to find the window with the highest affinity in each image of the sequence and replace it with the window in the reference image. The affinity value is based on polynomial approximation of the time series computed and the search is bounded by a search radius which defines the neighborhood of each window. The proposed approach is tested in ten 310-frame real cases in two experiments: the first one to determine the best values for the window size and the search radius and the second one to compare the best obtained results with respect to four registration methods found in the specialized literature. The obtained results show a robust and competitive performance of the proposed approach with a significant lower time with respect to the compared methods.

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