Comparative study of computational visual attention models on two-dimensional medical images

doi:10.1117/1.JMI.4.2.025503

Paper

Comparative study of computational visual attention models on two-dimensional medical images

Published Apr 1, 2017 · G. Wen, Brenda Rodriguez-Niño, Furkan Y. Pecen

Journal of Medical Imaging

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Abstract

Abstract. Computational modeling of visual attention is an active area of research. These models have been successfully employed in applications such as robotics. However, most computational models of visual attention are developed in the context of natural scenes, and their role with medical images is not well investigated. As radiologists interpret a large number of clinical images in a limited time, an efficient strategy to deploy their visual attention is necessary. Visual saliency maps, highlighting image regions that differ dramatically from their surroundings, are expected to be predictive of where radiologists fixate their gaze. We compared 16 state-of-art saliency models over three medical imaging modalities. The estimated saliency maps were evaluated against radiologists’ eye movements. The results show that the models achieved competitive accuracy using three metrics, but the rank order of the models varied significantly across the three modalities. Moreover, the model ranks on the medical images were all considerably different from the model ranks on the benchmark MIT300 dataset of natural images. Thus, modality-specific tuning of saliency models is necessary to make them valuable for applications in fields such as medical image compression and radiology education.

Study Snapshot

Key takeawayModality-specific tuning of visual attention models is necessary for efficient deployment in medical images, as their accuracy varies across different imaging modalities.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Comparative study of computational visual attention models on two-dimensional medical images

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