Compression of digital chest radiographs with a mixture of principal components neural network: evaluation of performance.

Abstract

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-Loève 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.