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Proceedings Paper

Effect of lesion blurring on observer performance in AFC experiments using chest CT images
Author(s): Kent M. Ogden; Danielle Williams; Dalanda Jalloh; Marsha Roskopf
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Paper Abstract

The goal was to analyze the influence of blurring of artificial lesions on observer performance during AFC experiments in chest CT images. Lesion images were generated by scanning Teflon rods of multiple sizes (3/16", 1/4", 5/16", 3/8", and 1/2") in a General Electric VCT scanner. Images were reconstructed using Bone and Detail reconstruction algorithms and cropped for use in AFC experiments. Three sets of artificial lesions (simple disks) were generated mathematically at the same sizes as the Teflon lesions, with two of the sets blurred with 3x3 and 5x5 averaging kernels. All lesions were scaled to have the same maximum intensity. Approximately 180 normal chest CT images (both Bone and Detail algorithm) were collected under IRB exemption for use in 2-AFC experiments. Two observers conducted AFC experiments using the Teflon lesions with the appropriate CT images, and using the artificial lesions in both sets of CT images. A performance metric was calculated that allowed comparison of experimental results. For Bone algorithm images, the Teflon and un-blurred lesions produced equivalent performance. Performance was significantly worse using the blurred lesions. For the Detail algorithm images, un-blurred lesion performance was significantly better than with the Teflon lesion. The performance using the 3x3-blurred lesions was the closest to the Teflon lesion performance, though it was slightly worse. Using these results, it is possible to design artificial lesions of any size for use in AFC experiments that will result in observer performance equivalent to that when using lesions derived from physical phantoms.

Paper Details

Date Published: 22 February 2012
PDF: 6 pages
Proc. SPIE 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment, 83181B (22 February 2012); doi: 10.1117/12.911449
Show Author Affiliations
Kent M. Ogden, SUNY Upstate Medical Univ. (United States)
Danielle Williams, SUNY Upstate Medical Univ. (United States)
Dalanda Jalloh, SUNY Upstate Medical Univ. (United States)
Marsha Roskopf, SUNY Upstate Medical Univ. (United States)


Published in SPIE Proceedings Vol. 8318:
Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Claudia R. Mello-Thoms, Editor(s)

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