Paper
12 June 1986 Size Discrimination Of Features On CT Images
Philip F. Judy, Richard G. Swensson
Author Affiliations +
Proceedings Volume 0626, Application of Optical Instrumentation in Medicine XIV and Picture Archiving and Communication Systems; (1986) https://doi.org/10.1117/12.975397
Event: Application of Optical Instrumentation in Medicine XIV and Picture Archiving and Communication Systems (PACS IV) for Medical Applications, 1986, Newport Beach, CA, United States
Abstract
Observers rated the likelihood that two circular features on a CT image differed in size, or the likelihood that a ring was present on a CT image. The ring features were produced by subtracting the image profiles of the two disks that had to be distinguished. Since a physical cross-correlator used the same formal calculations on the image pixel values in both tasks, the signal-to-noise ratio (SNR) for the size discrimination task could be predicted from the values of SNR in the ring discrimination task. The 15 sets of images in both tasks studied three levels of feature contrast for discrimination with the relative difference in the disks' diameters adjusted to vary observers' performance over a measureable range. Indices of performance accuracy, as estimated from observers' ROC curves, were well predicted by assuming that they were proportional to the calculated values of Task SNR. The fitted constants of proportionality were similiar in both the size-discrimination (0.470) and ring-detection (0.556) tasks. Thus the human. observer's processing of image information in both the size-discrimination and ring-detection tasks was similar to, but less efficient than, that of the physical cross-correlator.
© (1986) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Philip F. Judy and Richard G. Swensson "Size Discrimination Of Features On CT Images", Proc. SPIE 0626, Application of Optical Instrumentation in Medicine XIV and Picture Archiving and Communication Systems, (12 June 1986); https://doi.org/10.1117/12.975397
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Cited by 3 scholarly publications.
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KEYWORDS
Signal to noise ratio

Computed tomography

Data modeling

Binary data

Error analysis

Liver

Medicine

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