Medical image compression with neural nets

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.