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Proceedings Paper

Assessment of low-contrast detectability for compressed digital chest images
Author(s): Larry T. Cook; Michael F. Insana; Michael A. McFadden; Timothy J. Hall; Glendon G. Cox
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Paper Abstract

The ability of human observers to detect low-contrast targets in screen-film (SF) images, computed radiographic (CR) images, and compressed CR images was measured using contrast detail (CD) analysis. The results of these studies were used to design a two- alternative forced-choice (2AFC) experiment to investigate the detectability of nodules in adult chest radiographs. CD curves for a common screen-film system were compared with CR images compressed up to 125:1. Data from clinical chest exams were used to define a CD region of clinical interest that sufficiently challenged the observer. From that data, simulated lesions were introduced into 100 normal CR chest films, and forced-choice observer performance studies were performed. CR images were compressed using a full-frame discrete cosine transform (FDCT) technique, where the 2D Fourier space was divided into four areas of different quantization depending on the cumulative power spectrum (energy) of each image. The characteristic curve of the CR images was adjusted so that optical densities matched those of the SF system. The CD curves for SF and uncompressed CR systems were statistically equivalent. The slope of the CD curve for each was - 1.0 as predicted by the Rose model. There was a significant degradation in detection found for CR images compressed to 125:1. Furthermore, contrast-detail analysis demonstrated that many pulmonary nodules encountered in clinical practice are significantly above the average observer threshold for detection. We designed a 2AFC observer study using simulated 1-cm lesions introduced into normal CR chest radiographs. Detectability was reduced for all compressed CR radiographs.

Paper Details

Date Published: 1 April 1994
PDF: 11 pages
Proc. SPIE 2166, Medical Imaging 1994: Image Perception, (1 April 1994); doi: 10.1117/12.171737
Show Author Affiliations
Larry T. Cook, Univ. of Kansas Medical Ctr. (United States)
Michael F. Insana, Univ. of Kansas Medical Ctr. (United States)
Michael A. McFadden, Univ. of Kansas Medical Ctr. (United States)
Timothy J. Hall, Univ. of Kansas Medical Ctr. (United States)
Glendon G. Cox, Univ. of Kansas Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 2166:
Medical Imaging 1994: Image Perception
Harold L. Kundel, Editor(s)

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