Abstract

Visual-search (VS) model observers have the potential to provide reliable predictions of human-observer performance in detection-localization tasks. The purpose of this work was to examine some characteristics of human gaze on breast images with the goal of informing the design of our VS observers. Using a helmet-mounted eye-tracking system, we recorded the movement of gaze from human observers as they searched for masses in sets of 2D digital breast tomosynthesis (DBT) images. The masses in this study were of a single profile. The DBT images were extracted from image volumes reconstructed with the filtered backprojection method. Fixation times associated with observer points of interest were computed from the observer data. We used the k-mean clustering algorithm to get dwell times of gaze data. The dwell times were then compared to sets of morphological feature values extracted from the images. These features, extracted as cross correlations involving the mass profile and the test image, included the matched filter (MF), gradient MF, Laplacian MF, and adaptive MF. The adaptive MF combining four feature maps was computed using a hotelling discriminant generated from training data. For this investigation, we computed correlation coefficients between the fixation times and the feature values. We also conducted a significance test by computing p-values of correlation coefficients for five features. Of all these features, the adaptive MF provided the highest correlation coefficients for DBT images with different densities.

© 2017 Optical Society of America

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