A supervised learning approach to landmark-based elastic biomedical image registration and interpolation

Author

Wachowiak, Mark P. ; Smolikovà, Renata ; Zurada, Jacek M. ; Elmaghraby, Adel S.

Author_Institution

Comput. Sci. & Eng. Program, Louisville Univ., KY, USA

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1625

Lastpage

1630

Abstract

Biomedical image registration often requires local elastic matching after initial global alignment. Due to their universal approximation property, neural networks may be used for landmark-based elastic registration. A supervised learning approach using backpropagation, Bayesian regularization, Gauss-sigmoid networks, and radial basis function networks is presented for 2D elastic registration

Keywords

backpropagation; belief networks; biomedical imaging; image registration; interpolation; radial basis function networks; Bayesian regularization; Gauss-sigmoid networks; backpropagation; interpolation; landmark-based elastic biomedical image registration; radial basis function networks; supervised learning approach; universal approximation property; Backpropagation; Bayesian methods; Biomedical engineering; Biomedical imaging; Computer science; Gaussian processes; Interpolation; Polynomials; Radial basis function networks; Supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007761

Filename

1007761