Foveated model observer to predict human search performance on virtual digital breast tomosynthesis phantoms

Miguel A. Lago; Bruno Barufaldi; Predrag R. Bakic; Craig K. Abbey; Andrew D. Maidment; Miguel P. Eckstein

doi:10.1117/12.2550485

16 March 2020 Foveated model observer to predict human search performance on virtual digital breast tomosynthesis phantoms

Miguel A. Lago, Bruno Barufaldi, Predrag R. Bakic, Craig K. Abbey, Andrew D. Maidment, Miguel P. Eckstein

Author Affiliations +

Proceedings Volume 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment; 113160V (2020) https://doi.org/10.1117/12.2550485
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Model observers for image quality assessment have been extensively used in the field of medical imaging. The majority of model observer developments have involved signal detection tasks with a few number of signal locations and models that have not explicitly incorporated the varying resolution in visual processing across the visual field (foveated vision). Here, we evaluate search performance by human and model observers in 3D search and 2D single-slice search with DBT virtual phantoms images for a simulated single simulated macrocalcification. We compare the ability of a Channelized Hotelling Observer model (CHO) and a Foveated Channelized Hotelling model (FCHO) in predicting human performance across 2D and 3D search. Human performance detecting the macrocalcification signal was significantly higher in 2D than in 3D (proportion correct, PC = 0.89 vs 0.68). However, the CHO model predicted a lower performance in 2D than in 3D search (PC = 0.84 vs 0.93). The FCHO, that processes the visual field with lowering spatial detail as the distance increases from the point of fixation, executes eye movements, and scrolls across slices, correctly predicts the relative performance for the detection of the macrocalcification in 2D and 3D search (PC = 0.92 vs 0.59). These results suggest that foveation is a key component for model observers when predicting human performance detecting small signals in DBT search.

Conference Presentation

Citation Download Citation

Miguel A. Lago, Bruno Barufaldi, Predrag R. Bakic, Craig K. Abbey, Andrew D. Maidment, and Miguel P. Eckstein "Foveated model observer to predict human search performance on virtual digital breast tomosynthesis phantoms", Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 113160V (16 March 2020); https://doi.org/10.1117/12.2550485

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available