scholarly journals Technical Note—Least-Squares Image Registration

1991 ◽  
Vol 3 (2) ◽  
pp. 169-172 ◽  
Author(s):  
Karel Zikan
Keyword(s):  
Image Registration ◽  
Least Squares ◽  
Technical Note

Technical note: Validation of 3D ultrasound for image registration during oncological liver surgery

2021 ◽  
Author(s):  
Jasper N. Smit ◽  
Koert F.D. Kuhlmann ◽  
Bart R. Thomson ◽  
Niels F.M. Kok ◽  
Matteo Fusaglia ◽  
...  
Keyword(s):  
Image Registration ◽  
Liver Surgery ◽  
3D Ultrasound ◽  
Technical Note

scholarly journals Technical note: A novel boundary condition using contact elements for finite element based deformable image registration

2004 ◽  
Vol 31 (9) ◽  
pp. 2412-2415 ◽  
Author(s):  
Tiezhi Zhang ◽  
Nigel P. Orton ◽  
T. Rockwell Mackie ◽  
Bhudatt R. Paliwal
Keyword(s):  
Boundary Condition ◽  
Finite Element ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Technical Note

scholarly journals Technical Note: Review of methods for linear least-squares fitting of data and application to atmospheric chemistry problems

2008 ◽  
Vol 8 (2) ◽  
pp. 6409-6436 ◽  
Author(s):  
C. A. Cantrell
Keyword(s):  
Least Squares ◽  
Atmospheric Chemistry ◽  
Least Squares Method ◽  
Technical Note ◽  
Routine Practice ◽  
Data Sets ◽  
Straight Line ◽  
Sample Data ◽  
Straight Lines ◽  
Best Fit

Abstract. The representation of data, whether geophysical observations, numerical model output or laboratory results, by a best fit straight line is a routine practice in the geosciences and other fields. While the literature is full of detailed analyses of procedures for fitting straight lines to values with uncertainties, a surprising number of scientists blindly use the standard least squares method, such as found on calculators and in spreadsheet programs, that assumes no uncertainties in the x values. Here, the available procedures for estimating the best fit straight line to data, including those applicable to situations for uncertainties present in both the x and y variables, are reviewed. Representative methods that are presented in the literature for bivariate weighted fits are compared using several sample data sets, and guidance is presented as to when the somewhat more involved iterative methods are required, or when the standard least-squares procedure would be expected to be satisfactory. A spreadsheet-based template is made available that employs one method for bivariate fitting.

Technical Note. Effect of Geometrical Distortion Correction in MR on Image Registration Accuracy

1996 ◽  
Vol 20 (4) ◽  
pp. 666-679 ◽  
Author(s):  
Calvin R. Maurer ◽  
Georges B. Aboutanos ◽  
Benoit M. Dawant ◽  
Srikanth Gadamsetty ◽  
Richard A. Margolin ◽  
...  
Keyword(s):  
Image Registration ◽  
Technical Note ◽  
Distortion Correction ◽  
Registration Accuracy ◽  
Geometrical Distortion

scholarly journals A least-squares functional for joint exit wave reconstruction and image registration

2019 ◽  
Vol 35 (5) ◽  
pp. 054004
Author(s):  
Christian Doberstein ◽  
Benjamin Berkels
Keyword(s):  
Image Registration ◽  
Least Squares

scholarly journals Technical Note: Review of methods for linear least-squares fitting of data and application to atmospheric chemistry problems

2008 ◽  
Vol 8 (17) ◽  
pp. 5477-5487 ◽  
Author(s):  
C. A. Cantrell
Keyword(s):  
Least Squares ◽  
Atmospheric Chemistry ◽  
Least Squares Method ◽  
Technical Note ◽  
Routine Practice ◽  
Data Sets ◽  
Straight Line ◽  
Sample Data ◽  
Straight Lines ◽  
Best Fit

Abstract. The representation of data, whether geophysical observations, numerical model output or laboratory results, by a best fit straight line is a routine practice in the geosciences and other fields. While the literature is full of detailed analyses of procedures for fitting straight lines to values with uncertainties, a surprising number of scientists blindly use the standard least-squares method, such as found on calculators and in spreadsheet programs, that assumes no uncertainties in the x values. Here, the available procedures for estimating the best fit straight line to data, including those applicable to situations for uncertainties present in both the x and y variables, are reviewed. Representative methods that are presented in the literature for bivariate weighted fits are compared using several sample data sets, and guidance is presented as to when the somewhat more involved iterative methods are required, or when the standard least-squares procedure would be expected to be satisfactory. A spreadsheet-based template is made available that employs one method for bivariate fitting.

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