The objective of this work was to test the capabilities of visual-search (VS) model observers for target classification. In this paper, a localization and classification ROC study was conducted with simulated single-pinhole nuclear medicine images. The images featured two sizes of Gaussian targets in Gaussian lumpy backgrounds, with one target twice the size of the other. Pinhole size was a study variable. The VS observer performed both the localization and classification. Three human observers also participated in the study. The trends in localization performance as a function of pinhole size for the VS and average human results were in good agreement. For the classification task, the VS and human observers performed on par, but with substantial differences in how they were affected by pinhole size. The VS observer correctly classified the smaller target less often than the larger target even though both targets were correctly localized with the same frequency.
Kheya Banerjee and Howard C. Gifford, "Lesion classification with a visual-search model observer
," Proc. SPIE 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, 105770Y (Presented at SPIE Medical Imaging: February 12, 2018; Published: 7 March 2018); https://doi.org/10.1117/12.2294946.
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Study of self-shadowing effect as a simple means to realize nanostructured thin films and layers with special attentions to birefringent obliquely deposited thin films and photo-luminescent porous silicon