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Image quality assessment is crucial for evaluating and optimizing imaging systems, especially in diagnostic tasks involving expert human readers. In this work, we propose a threshold mechanism that may enhance the ability of model observers to predict human performance in diagnostic tasks. The threshold sets lower limits on acceptable feature values extracted from an image. This mechanism was tested with existing prewhitening and nonprewhitening visual-search models for lesion search in lumpy-background images. The images simulated single-pinhole planar imaging with a radionuclide. Pinhole size was a study variable. A localization ROC (LROC) study format was used. A set of three Gabor functions defined the feature space. One study tested the two model observers with and without optimized thresholding. A second study with the prewhitening model examined the effect of thresholding on the training requirements needed to obtain stable estimates of LROC performance. With regard to agreement with human observers for the same search task, thresholding overcame substantial limitations of the nonprewhitening observer. Thresholding also consistently reduced the numbers of training images required to compute stable estimates of performance with the prewhitening visual-search model. Overall, these results point to possible benefits for low-resource application of these model observers.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Hongwei Lin andHoward C. Gifford
"Addition of a threshold mechanism to model observers for medical image quality assessment", Proc. SPIE 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment, 129290D (29 March 2024); https://doi.org/10.1117/12.3008279
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Hongwei Lin, Howard C. Gifford, "Addition of a threshold mechanism to model observers for medical image quality assessment," Proc. SPIE 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment, 129290D (29 March 2024); https://doi.org/10.1117/12.3008279