Abstract
Previous studies have compared the performance of human observers to the performance of human observers using CAD. Here we compare the performance of human observers to Hologic's ImageChecker CAD system using a set of 162 images with simulated calcification clusters. The quality of the images was reduced to create four other image sets at different image qualities. These were analysed by the CAD system and the relevant information from the resulting DICOM structured reports was parsed. At the highest image quality level the figure of merit for the CAD was 0.82 and 0.84 for the humans. At the lowest image quality level the figure of merit for the CAD and humans were 0.62 and 0.55 respectively. At each image quality level there was no significant difference (p>0.05). The effect of changes in image quality on calcification detection was similar for human observers and the CAD system. © 2014 Springer International Publishing.
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CITATION STYLE
Looney, P. T., Warren, L. M., Astley, S. M., & Young, K. C. (2014). Comparison of calcification cluster detection by CAD and human observers at different image quality levels. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8539 LNCS, pp. 643–649). Springer Verlag. https://doi.org/10.1007/978-3-319-07887-8_89
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