Title :
Medical image compression with neural nets
Author :
Steudel, A. ; Ortmann, S. ; Glesner, M.
Author_Institution :
Inst. of Microelectron. Syst., Darmstadt Univ. of Technol., Germany
Abstract :
A nonlinear 5 layer artificial neural autoencoder network for image data compression is constructed and trained using the back propagation algorithm and medical CT images. The influence of linear and nonlinear pre/postprocessing operations is studied as well as an alternative compression scheme. Important implementational issues of neural networks are addressed as well as autoencoder issues. One of the results of this work is a compression/decompression tool that provides maximum flexibility and can be used independently from the training environment
Keywords :
backpropagation; computerised tomography; data compression; encoding; image coding; medical image processing; neural nets; alternative compression scheme; autoencoder issues; back propagation algorithm; compression/decompression tool; image data compression; implementational issues; maximum flexibility; medical CT images; medical image compression; neural nets; nonlinear 5 layer artificial neural autoencoder network; nonlinear pre/postprocessing operations; training environment; Artificial neural networks; Back; Biomedical imaging; Digital images; Image coding; Image reconstruction; Medical diagnostic imaging; Neural networks; Spatial resolution; X-ray imaging;
Conference_Titel :
Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95., Third International Symposium on
Conference_Location :
College Park, MD
Print_ISBN :
0-8186-7126-2
DOI :
10.1109/ISUMA.1995.527758
