Paper
6 April 2005 Contrast-detail curves in chest radiography
Kent Ogden, Ernest Scalzetti, Walter Huda, Jasjeet Saluja, Robert Lavallee
Author Affiliations +
Abstract
We investigated how size and lesion location affect detection of simulated mass lesions in chest radiography. Simulated lesions were added to the center of 10 cm x 10 cm regions of digital chest radiographs, and used in 4-Alternative Forced-Choice (4-AFC) experiments. We determined the lesion contrast required to achieve a 92% correct detection rate I(92%). The mass size was manipulated to range from 1 to 10 mm, and we investigated lesion detection in the lung apex, hilar region, and in the sub-diaphragmatic region. In these experiments, the observer obtained I(92%) from randomized repeats obtained at each of seven lesion sizes, with the results plotted as I(92%) versus lesion size. In addition we investigated the effect of using the same background in the four 4-AFC experiments (twinned) and random backgrounds from the same anatomical region taken from 20 different radiographs. In all three anatomical regions investigated, the slopes of the contrast detail curve for the random background experiments were negative for lesion sizes less than 2.5, 3.5, and 5.5 mm in the hilar (slope of -0.26), apex (slope of -0.54), and sub-diaphragmatic (slope of -0.53) regions, respectively. For lesion sizes greater than these, the slopes were 0.34, 0.23, and 0.40 in the hilar, apex, and sub-diaphragmatic regions, respectively. The positive slopes for portions of the contrast-detail curves in chest radiography are a result of the anatomical background, and show that larger lesions require more contrast for visualization.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kent Ogden, Ernest Scalzetti, Walter Huda, Jasjeet Saluja, and Robert Lavallee "Contrast-detail curves in chest radiography", Proc. SPIE 5749, Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment, (6 April 2005); https://doi.org/10.1117/12.595578
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KEYWORDS
Radiography

Chest

Lung

Chest imaging

Data modeling

Lung cancer

Image processing

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