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24 March 2016 Luminance level of a monitor: influence on detectability and detection rate of breast cancer in 2D mammography
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Abstract
Purpose: To evaluate lesion detectability and reading time as a function of luminance level of the monitor. Material and Methods: 3D mass models and microcalcification clusters were simulated into ROIs of for processing mammograms. Randomly selected ROIs were subdivided in three groups according to their background glandularity: high (>30%), medium (15-30%) and low (<15%). 6 non-spiculated masses (9 – 11mm), 6 spiculated masses (5 – 7mm) and 6 microcalcification clusters (2 – 4mm) were scaled in 3D to create a range of sizes. The linear attenuation coefficient (AC) of the masses was adjusted from 100% glandular tissue to 90%, 80%, 70%, to create different contrasts. Six physicists read the full database on Barco’s Coronis Uniti monitor for four different luminance levels (300, 800, 1000 and 1200 Cd/m2), using a 4-AFC tool. Percentage correct (PC) and time were computed for all different conditions. A paired t-test was performed to evaluate the effect of luminance on PC and time. A multi-factorial analysis was performed using MANOVA.. Results: Paired t-test indicated a statistically significant difference for the average time per session between 300 and 1200; 800 and 1200; 1000 and 1200 Cd/m2, for all participants combined. There was no effect on PC. MANOVA denoted significantly lower reading times for high glandularity images at 1200 Cd/m2. Both types of masses were significantly faster detected at 1200 Cd/m2, for the contrast study. In the size study, microcalcification clusters and spiculated masses had a significantly higher detection rate at 1200 Cd/m2. Conclusion: These results demonstrate a significant decrease in reading time, while detectability remained constant.
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Frédéric Bemelmans, Alaleh Rashidnasab, Frédérique Chesterman, Tom Kimpe, and Hilde Bosmans "Luminance level of a monitor: influence on detectability and detection rate of breast cancer in 2D mammography", Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 97870Y (24 March 2016); https://doi.org/10.1117/12.2216472
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