RadioGraphics, Vol 16, 1481-1488, Copyright © 1996 by Radiological Society of North America
Compression of digital chest radiographs with a mixture of principal components neural network: evaluation of performance
RD Dony, CL Coblentz, C Nabmias and S Haykin
Department of Radiology and Nuclear Medicine, McMaster University Medical Centre, Hamilton, Ontario, Canada.
The performance of a new, neural network-based image compression method was
evaluated on digital radiographs for use in an educational environment. The
network uses a mixture of principal components (MPC) representation to
effect optimally adaptive transform coding of an image and has significant
computational advantages over other techniques. Nine representative digital
chest radiographs were compressed 10:1, 20:1, 30:1, and 40:1 with the MPC
method. The five versions of each image, including the original, were shown
simultaneously, in random order, to each of seven radiologists, who rated
each one on a five-point scale for image quality and visibility of
pathologic conditions. One radiologist also ranked four versions of each of
the nine images in terms of the severity of distortion: The four versions
represented 30:1 and 40:1 compression with the MPC method and with the
classic Karhunen-Loeve transform (KLT). Only for the images compressed 40:1
with the MPC method were there any unacceptable ratings. Nevertheless, the
images compressed 40:1 received a top score in 26%-33% of the evaluations.
Images compressed with the MPC method were rated better than or as good as
images compressed with the KLT technique 17 of 18 times. Four of nine
times, images compressed 40:1 with the MPC method were rated as good as or
better than images compressed 30:1 with the KLT technique.
