title: Fixated and Not Fixated Regions of Mammograms: A Higher-Order Statistical Analysis of Visual Search Behavior.
abstract: Visual search is an inhomogeneous yet efficient sampling process accomplished by the saccades and the central (foveal) vision. Areas that attract the central vision have been studied for errors in interpretation of medical imaging. In this study, we extend existing visual search studies to understand what characterizes areas that receive direct visual attention and elicit a mark by the radiologist (True and False Positive decisions) from those that elicit a mark but were captured by the peripheral vision. We also investigate if there are any differences between these areas and those that are never fixated by radiologists.
  
  
  
  Eight radiologists participated in this fully crossed multi-reader multi-case visual search study of digital mammography (DM) involving 120 two-view cases (59 cancers). From these DM images, 3 types of areas, namely Fixated Clusters (FC), Marked Peripherally Fixated Clusters (MPFC) and Never Fixated Clusters (NFC), were extracted and analysed using statistical information theory (in the form of third and fourth-order cumulants and polyspectrum [specifically bispectrum and trispectrum]) in addition to traditional second-order statistics (in the form of power spectrum) and other nonspectral features to characterize these types of areas.
  
  
  
  Our results suggest that energy profiles of FC, MPFC, and NFC areas are distinct. We found evidence that energy profiles and dwell time of these areas influence radiologists' decisions (and confidence in such decisions). We also noted that foveated areas are selected on the basis of being most informative.
  
  
  
  We show that properties of these areas influence radiologists' decisions and their confidence in the decisions made.
authors.affiliation: Medical Image Perception and Optimization Group (MIOPeG), Faculty of Health Sciences, University of Sydney, Lidcombe, NSW 2141, Australia. Electronic address: smal5514@uni.sydney.edu.au.
authors.forename: Suneeta
authors.initials: S
authors.lastname: Mall
authors.affiliation: Medical Image Perception and Optimization Group (MIOPeG), Faculty of Health Sciences, University of Sydney, Lidcombe, NSW 2141, Australia.
authors.forename: Patrick
authors.initials: P
authors.lastname: Brennan
authors.affiliation: Medical Image Perception and Optimization Group (MIOPeG), Faculty of Health Sciences, University of Sydney, Lidcombe, NSW 2141, Australia.
authors.forename: Claudia
authors.initials: C
authors.lastname: Mello-Thoms
mesh_terms: D000070379:Attentional Bias / Q000502:physiology; D001315:Australia; D001940:Breast / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000175:diagnosis*; D000066491:Clinical Decision-Making / Q000379:methods; D003951:Diagnostic Errors / Q000517:prevention & control* / Q000523:psychology; D005260:Female; D005584:Fovea Centralis / Q000502:physiology; D006801:Humans; D008327:Mammography / Q000379:methods*; D015588:Observer Variation; D010364:Pattern Recognition, Visual / Q000502:physiology*; D000072177:Radiologists / Q000523:psychology / Q000706:statistics & numerical data
keywords: Visual search; high-order analysis; mammography; perception; spatial frequency analysis