Barry J. Sullivan

New termination rule for linear iterative image restoration algorithms

Every iterative restoration algorithm requires some performance measure to determine the termination point for the iteration. Typically, the termination rule is based on a measure of the residual or the change in the solution from one iteration to the next. A better rule would be one that terminates the iteration when the distance between the original undistorted image and restored image is minimized in some sense. In this paper, we present a performance measure that estimates this distance without prior knowledge of the original image. Our measure relies on evaluating a spectral filter function at each step of the iteration. These functions describe the solution at each step in terms of the singular value decomposition of the system matrix. As such, spectral filter functions provide valuable insight into the behavior of an iteration as well as a means of defining a termination rule. We develop a general technique for determining the spectral filter functions for a given iteration, which we demonstrate by applying it to a linear iterative image restoration algorithm.

Adaptation and evaluation of JPEG-based compression for radiographic images

The transmission of digitized medical images over the existing telecommunications infrastructure presents a formidable challenge. To achieve delivery times on the order of seconds--as opposed to minutes or hours--for large-format high-resolution images, data compression on the order of 25:1 is necessary. This degree of data compression cannot be reached with lossless techniques. This paper reports on an adaptation of a standardized technique for lossy image compression (the JPEG approach), which provides high compression ratios for radiographic images with minimal apparent loss of diagnostic quality.

Relative effects of resolution and quantization on the quality of compressed medical images

Medical image scanners produce digitized information at different spatial resolutions, and this affects the perceived image quality after image compression. Quality of decompressed medical images is assessed by the extent to which diagnostic information is affected by compression, and the medical interpretation requires participation of medical experts in observer studies. We undertook a study to investigate the effect of resolution from digitization on medical image compression, in the context of enabling image transmission using low to moderate bandwidth telecommunication facilities. The test material consisted of a series of adult chest radiographs containing multiple subtle abnormalities. In this paper, the effects of sampling resolution and frequency component quantization on image quality are compared, and issues for further investigation in general image compression are discussed.<<ETX>>