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Proceedings Paper

Image similarity ranking of focal computed tomography liver lesions using a 2AFC technique
Author(s): Jessica Faruque; Sameer Antani; Rodney Long; Lauren Kim; George Thoma
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Paper Abstract

Content-based image retrieval (CBIR) for radiological images has experienced massive growth over the past two decades, and shows great potential as a tool for use in precision medicine. A recurring challenge in CBIR evaluation has been in obtaining reference sets of images from human viewers of the system. Our work seeks to determine the feasibility of creating a reference set from images ranked by similarity from human viewers of the images. We obtained 2 sets each of 10 images of CT focal liver lesions from a database of open-access publications with and without markings showing the region containing the lesions, respectively. We created 2 sets of all 45 pair-wise combinations of the images, and displayed them to 10 volunteers, of which 2 had medical training. We used a Two-Alternative Forced Choice (2AFC) paradigm to obtain complete rankings of similarity levels in these image pairs. Analysis showed that inter-reader agreement for rankings ranged from Tau=0.21-0.69 (median=0.37) for the image pairs without any markings, and Tau=0.21-0.57 (median=0.33) for the image pairs with markings. A comparison of the regions of interests drawn by the study participants outlining the lesions in images without markings showed that participants tended to agree on images containing a single focal lesion of a single density, and inter-reader agreement for image rankings in which the regions of interest agree ranged from Tau=0.39-0.85 (median=0.58). These results show that the use of image ranking using 2AFC may be a feasible method for creating reference sets for CBIR system validation.

Paper Details

Date Published: 24 March 2016
PDF: 8 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 97870N (24 March 2016); doi: 10.1117/12.2217364
Show Author Affiliations
Jessica Faruque, National Institutes of Health (United States)
Sameer Antani, National Institutes of Health (United States)
Rodney Long, National Institutes of Health (United States)
Lauren Kim, National Institutes of Health (United States)
George Thoma, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 9787:
Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Matthew A. Kupinski, Editor(s)

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