Neural network-assisted effective lossy compression of medical images
Nikos G. Panagiotidis, Dimitris Kalogeras, Stefanos Kollias, Andreas Stafylopatis · Proceedings of the IEEE · 1996
A neural network architecture is proposed and shown to be very effective in performing lossy compression of medical images. A novel ROI-JPEG technique is introduced as the coding platform, in which the neural architecture adoptively selects regions of interest (ROI's) in the images. By letting the selected ROI's be coded with high quality, in contrast to the rest of image areas, high compression ratios are achieved, while retaining the significant (from medical point of view) image content. The performance of the method is illustrated by means of experimental results in real life problems taken from pathology and telemedicine applications. © 1996 IEEE.