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Proceedings Paper

Detection of single cells: an observer study
Author(s): Nancy L Ford; Steven I Pollmann; Damiaan F Habets; David W Holdsworth
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Paper Abstract

Recent advances in imaging technology have brought high-resolution imaging into the practical laboratory setting. As a result, there has been increasing interest in imaging molecular and cellular processes in live animals. To image a single cell, a contrast medium, radiolabel or metallic label is inserted into the cell, which is then introduced into the animal. How well the cell is visualized depends upon the contrast-to-noise ratio between the cell and the surrounding tissues, along with other factors such as the amount of signal present in each voxel of the image and the size of the contrast-enhanced region, as compared with the image dimensions. Through observer studies, we are investigating the detectability of a single cell in an image. We synthesized uniform volumetric datasets with Gaussian distributed noise and altered a single voxel to reflect one of 5 different contrast-to-noise ratios (CNRs) to create the cell-labeled image. The maximum intensity projection was acquired through each image. For each dataset, a high-contrast signal-only image was flanked on either side by the noise image and the cell-labeled image to create an image triplet. The observer task was to locate the cell-labeled image in a two-alternative forced choice study.

Paper Details

Date Published: 4 May 2004
PDF: 8 pages
Proc. SPIE 5372, Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment, (4 May 2004); doi: 10.1117/12.535726
Show Author Affiliations
Nancy L Ford, Robarts Research Institute (Canada)
Univ. of Western Ontario (Canada)
Steven I Pollmann, Robarts Research Institute (Canada)
Damiaan F Habets, Robarts Research Institute (Canada)
Univ. of Western Ontario (Canada)
David W Holdsworth, Robarts Research Institute (Canada)
Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 5372:
Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment
Dev P. Chakraborty; Miguel P. Eckstein, Editor(s)

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