10 May 2017 Comparative study of computational visual attention models on two-dimensional medical images
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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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Gezheng Wen, Brenda Rodriguez-Niño, Furkan Y. Pecen, David J. Vining, Naveen Garg, Mia K. Markey, "Comparative study of computational visual attention models on two-dimensional medical images," Journal of Medical Imaging 4(2), 025503 (10 May 2017). https://doi.org/10.1117/1.JMI.4.2.025503 . Submission: Received: 24 February 2017; Accepted: 18 April 2017
Received: 24 February 2017; Accepted: 18 April 2017; Published: 10 May 2017
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