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

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4 Author(s)
M. P. Wachowiak ; Comput. Sci. & Eng. Program, Louisville Univ., KY, USA ; R. Smolikova ; J. M. Zurada ; A. S. Elmaghraby

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

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Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on  (Volume:2 )

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