Literature DB >> 28139426

Fixated and Not Fixated Regions of Mammograms: A Higher-Order Statistical Analysis of Visual Search Behavior.

Suneeta Mall1, Patrick Brennan2, Claudia Mello-Thoms2.   

Abstract

RATIONALE AND
OBJECTIVES: 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.
MATERIALS AND METHODS: 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.
RESULTS: 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.
CONCLUSION: We show that properties of these areas influence radiologists' decisions and their confidence in the decisions made.
Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Visual search; high-order analysis; mammography; perception; spatial frequency analysis

Mesh:

Year:  2017        PMID: 28139426     DOI: 10.1016/j.acra.2016.11.020

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  2 in total

1.  Can a Machine Learn from Radiologists' Visual Search Behaviour and Their Interpretation of Mammograms-a Deep-Learning Study.

Authors:  Suneeta Mall; Patrick C Brennan; Claudia Mello-Thoms
Journal:  J Digit Imaging       Date:  2019-10       Impact factor: 4.056

2.  Modeling visual search behavior of breast radiologists using a deep convolution neural network.

Authors:  Suneeta Mall; Patrick C Brennan; Claudia Mello-Thoms
Journal:  J Med Imaging (Bellingham)       Date:  2018-08-11
  2 in total

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