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Proceedings Paper

Modeling decision-making in single- and multi-modal medical images
Author(s): R. L. Canosa; K. G. Baum
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Paper Abstract

This research introduces a mode-specific model of visual saliency that can be used to highlight likely lesion locations and potential errors (false positives and false negatives) in single-mode PET and MRI images and multi-modal fused PET/MRI images. Fused-modality digital images are a relatively recent technological improvement in medical imaging; therefore, a novel component of this research is to characterize the perceptual response to these fused images. Three different fusion techniques were compared to single-mode displays in terms of observer error rates using synthetic human brain images generated from an anthropomorphic phantom. An eye-tracking experiment was performed with naïve (non-radiologist) observers who viewed the single- and multi-modal images. The eye-tracking data allowed the errors to be classified into four categories: false positives, search errors (false negatives never fixated), recognition errors (false negatives fixated less than 350 milliseconds), and decision errors (false negatives fixated greater than 350 milliseconds). A saliency model consisting of a set of differentially weighted low-level feature maps is derived from the known error and ground truth locations extracted from a subset of the test images for each modality. The saliency model shows that lesion and error locations attract visual attention according to low-level image features such as color, luminance, and texture.

Paper Details

Date Published: 12 March 2009
PDF: 12 pages
Proc. SPIE 7263, Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment, 72630J (12 March 2009); doi: 10.1117/12.811053
Show Author Affiliations
R. L. Canosa, Rochester Institute of Technology (United States)
K. G. Baum, Biomedical and Materials Multimodal Imaging Lab., Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 7263:
Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)

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