Generalized regression neural networks for biomedical image interpolation

A neural-statistical approach to biomedical image interpolation using generalized regression neural networks is presented. These networks are basis function architectures that approximate any arbitrary function between input and output vectors directly from training samples, and with any desired degree of smoothness, and thus can be used for multidimensional interpolation. Experimental results compare favorably with other interpolation techniques. Because of their flexibility and ease of training, generalized regression networks can be used to complement existing approaches, and can be especially useful for post-registration image fusion and visualization.

[1]  Jayaram K. Udupa,et al.  A task-specific evaluation of three-dimensional image interpolation techniques , 1999, IEEE Transactions on Medical Imaging.

[2]  Jonathon A. Chambers,et al.  Heuristic pattern correction scheme using adaptively trained generalized regression neural networks , 2001, IEEE Trans. Neural Networks.

[3]  Max A. Viergever,et al.  Interpolation Artefacts in Mutual Information-Based Image Registration , 2000, Comput. Vis. Image Underst..

[4]  José Carlos Príncipe,et al.  Super-resolution of images based on local correlations , 1999, IEEE Trans. Neural Networks.

[5]  J. Udupa,et al.  An objective comparison of 3-D image interpolation methods , 1998, IEEE Transactions on Medical Imaging.

[6]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[7]  Sheila S. Hemami,et al.  Regularity-preserving image interpolation , 1997, Proceedings of International Conference on Image Processing.

[8]  Nathalie Plaziac.  Image interpolation using neural networks , 1999, IEEE Trans. Image Process..

[9]  M. Unser,et al.  Interpolation Revisited , 2000, IEEE Trans. Medical Imaging.

[10]  K. Ferguson,et al.  Neural network prediction of obstructive sleep apnea from clinical criteria. , 1999, Chest.

[11]  Sheila S. Hemami,et al.  Regularity-preserving image interpolation , 1999, IEEE Trans. Image Process..

[12]  Y. L. Loukas,et al.  Radial basis function networks in host–guest interactions: instant and accurate formation constant calculations , 2000 .

[13]  M. Unser,et al.  Interpolation revisited [medical images application] , 2000, IEEE Transactions on Medical Imaging.

[14]  Sung-Jea Ko,et al.  Neural concurrent subsampling and interpolation for images , 1999 .

[15]  Gabriele Lohmann,et al.  Volumetric image analysis , 1998 .

[16]  Philip D. Wasserman,et al.  Advanced methods in neural computing , 1993, VNR computer library.

[17]  Sung-Jea Ko,et al.  Neural concurrent subsampling and interpolation for images , 1999, Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030).