Template models for forced-localization tasks

Craig K. Abbey; Frank W. Samuelson; Rongping Zeng; John M. Boone; Miguel P. Eckstein; Kyle Myers

doi:10.1117/12.2512302

4 March 2019 Template models for forced-localization tasks

Craig K. Abbey, Frank W. Samuelson, Rongping Zeng, John M. Boone, Miguel P. Eckstein, Kyle Myers

Author Affiliations +

Proceedings Volume 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment; 1095206 (2019) https://doi.org/10.1117/12.2512302
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

We have previously presented the results of a psychophysical study of forced localization performance in tasks that include ramp-spectrum noise as a model of CT acquisition noise. The study used efficiency analysis and the classification image technique to better understand the mechanisms of performance. In this work, we begin the process of developing models of observer performance for these tasks. Since the image statistics for these tasks are well defined, we can use the ensemble image mean and covariance to compute templates for several standard model observers (PWMF, NPW, NPWE, and four implementations of the CHO). The goal of this work is to compare these with the human observer classification images to see if they are using spatial weighting of images in similar ways to perform the tasks. The most direct comparison would be of a model-observer template to the average human-observer classification image. However it may be the case that the classification image estimation procedure introduces bias into the classification image. Additionally, classification images may have effects from internal noise. Thus we also explore a comparison of humans and models in which the model is elaborated with internal noise and used as a scanning linear filter to generate localization responses. A classification image is then estimated for the model and compared with human-observer classification images. We compare both performance and the classification-image profile to the human-subject data. We find very limited ability to fit both endpoints.

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Craig K. Abbey, Frank W. Samuelson, Rongping Zeng, John M. Boone, Miguel P. Eckstein, and Kyle Myers "Template models for forced-localization tasks", Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 1095206 (4 March 2019); https://doi.org/10.1117/12.2512302

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KEYWORDS

Image classification

Performance modeling

Data modeling

Eye models

Apodization

Image analysis

Statistical modeling

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