Selection of biorthogonal filters for image compression of MR images usingwavelet packets

We present an analysis of different filter banks for the compression of mag netic resonance (MR) images of the human brain using wavelet packets based on biorthogonal filters. Initially, peak signal to noise ratio (PSNR) and n ormalized root mean square (RMS) error criteria are calculated for a series of images compressed with a 33:1 ratio, using filter banks based on biorth ogonal wavelet packets. The results lead us to choose a few of these filter banks as optimal for image compression. One of these filters is employed t o compress several images at four different compression ratios: 12.5:1, 25: 1, 37.5:1 and 50:1. The quality of these images was evaluated by visual ana lysis by a group of seven experts who graded image quality on a 0-7 scale. Results show that using these filters, we can compress images to a rate of around 30:1 without introducing noticeable differences. Other applications for these filters are currently under study and include the compression/fus ion of MR image stacks in order to obtain even better reductions in the amo unt of data needed to reconstruct complete MRI studies. (C) 2000 IPEM. Publ ished by Elsevier Science Ltd. All rights reserved.