Changes in Visual Attention Patterns for Detection Tasks due to Dependencies on Signal and Background Spatial Frequencies
Year: 2026
Type: preprint
Abstract: We aim to investigate the impact of image and signal properties on visual attention mechanisms during a signal detection task in digital images. The application of insight yielded from this work spans many areas of digital imaging where signal or pattern recognition is involved in complex heterogenous background. We will use simulated tomographic breast images as the platform to investigate this question. While radiologists are highly effective at analyzing medical images to detect and diagnose diseases, misdiagnosis still occurs. These errors can stem from multiple sources such as the inherent properties of medical images and the accompanying signal characteristics play a pivotal role in visual search strategy while locating and identifying the disease. We selected digital breast tomosynthesis (DBT) images as a sample medical images with different breast densities and structures using digital breast phantoms (Bakic and XCAT). Two types of lesions (with distinct spatial frequency properties) were randomly inserted in the phantoms during projections to generate abnormal cases. Six non-radiologist human observers participated in observer study designed for a locating and detection of an 3-mm sphere lesion and 6-mm spicule lesion in reconstructed in-plane DBT slices. We collected eye-gaze data to estimate gaze metrics and to examine differences in visual attention mechanisms. Gaze analysis revealed that diagnostic response times were significantly longer in Bakic phantoms and high-density tissue backgrounds compared to XCAT breast phantoms (p ¡ 0.05) and lower-density backgrounds (p ¡ 0.01) respectively, indicating increased perceptual difficulty in anatomically complex scenes. Observers made longer fixations on spiculated lesions compared to spherical lesions (p ¡ 0.01), suggesting that lesion morphology modulates visual attention allocation. Across all attention stages-search, recognition, and decision-the small spherical lesion required higher contrast for successful detection, especially in the Bakic background where anatomical noise severely reduced its visibility. Error analysis showed that the majority of misdiagnosed cases (25%) occurred when the lesion was not visible, followed by decision-stage errors (11%), where the signal was seen but incorrectly judged. Conclusions: Diagnostic performance in complex visual environments is strongly constrained by later perceptual stages, with decision failures accounting for the largest proportion of errors. Lesion detectability is jointly influenced by both target morphology and background complexity, revealing a critical interaction between local signal features and global anatomical noise. Increased fixation duration on spiculated lesions suggests that visual attention is differentially engaged depending on lesion structure, potentially aiding recognition. These findings highlight the importance of perceptually informed design and training of computer aided diagnosis systems.
Source: PubMed
Authors: Kavuri, Amar, Gifford, Howard C., Das, Mini
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7124318037
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