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Proceedings Paper

Exploring the potential of analysing visual search behaviour data using FROC (free-response receiver operating characteristic) method: an initial study
Author(s): Leng Dong; Yan Chen; Sarah Dias; William Stone; Joseph Dias; John Rout; Alastair G. Gale
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Paper Abstract

Visual search techniques and FROC analysis have been widely used in radiology to understand medical image perceptual behaviour and diagnostic performance. The potential of exploiting the advantages of both methodologies is of great interest to medical researchers. In this study, eye tracking data of eight dental practitioners was investigated. The visual search measures and their analyses are considered here. Each participant interpreted 20 dental radiographs which were chosen by an expert dental radiologist. Various eye movement measurements were obtained based on image area of interest (AOI) information. FROC analysis was then carried out by using these eye movement measurements as a direct input source. The performance of FROC methods using different input parameters was tested. The results showed that there were significant differences in FROC measures, based on eye movement data, between groups with different experience levels. Namely, the area under the curve (AUC) score evidenced higher values for experienced group for the measurements of fixation and dwell time. Also, positive correlations were found for AUC scores between the eye movement data conducted FROC and rating based FROC. FROC analysis using eye movement measurements as input variables can act as a potential performance indicator to deliver assessment in medical imaging interpretation and assess training procedures. Visual search data analyses lead to new ways of combining eye movement data and FROC methods to provide an alternative dimension to assess performance and visual search behaviour in the area of medical imaging perceptual tasks.

Paper Details

Date Published: 10 March 2017
PDF: 5 pages
Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101360I (10 March 2017); doi: 10.1117/12.2255901
Show Author Affiliations
Leng Dong, Loughborough Univ. (United Kingdom)
Yan Chen, Loughborough Univ. (United Kingdom)
Sarah Dias, Univ. of Birmingham (United Kingdom)
William Stone, Univ. of Birmingham (United Kingdom)
Joseph Dias, Univ. of Leicester (United Kingdom)
John Rout, Univ. of Birmingham (United Kingdom)
Alastair G. Gale, Loughborough Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 10136:
Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
Matthew A. Kupinski; Robert M. Nishikawa, Editor(s)

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