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7 March 2018 Observer templates in 2D and 3D localization tasks
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In this study we examine search performance for 3D forced-localization tasks in Gaussian random textures in which subjects are able to freely scroll through the image as part of their search for the target. We also evaluate a 2D single-slice version of the same task for comparison. We analyze these experiments using both efficiency with respect to the Ideal Observer and the classification image technique, which directly estimates the weighting function used by observers for a task. We are particularly interested in whether subjects can efficiently integrate across multiple slices in depth as part of performing the localization task.

In the 3D tasks, the image display we use allows subjects to freely scroll through a volumetric image, and a localization response is made through a mouse-click on the image. The search region has a relatively modest size (approx. 8.8° visual angle). Localization responses are considered correct if they are close to the target center (within 6 voxels). The classification image methodology uses noise fields from the incorrect localizations to build an estimate of the weights used by the observer to perform the task. The basic idea is that incorrect localizations occur in regions of the image where the noise field matches the weighting profile, thereby eliciting a strong internal response.

The efficiency results indicate differences between 2D and 3D search tasks, with lower efficiency for large target in the 3D task. The classification images suggest that this finding can be explained by the lack of spatial integration across slices.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Craig K. Abbey, Miguel A. Lago, and Miguel P. Eckstein "Observer templates in 2D and 3D localization tasks", Proc. SPIE 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, 105770T (7 March 2018);

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