Abstract

Experienced radiologists are in short supply, and are sometimes called upon to read many images in a short amount of time. This leaves them with a limited amount of time to read images, and can lead to fatigue and stress which can be sources of error, as they overlook subtle abnormalities that they otherwise might not miss. Another factor in error rates is called satisfaction of search, where a radiologist misses a second (typically subtle) abnormality after finding the first. These types of errors are due primarily to a lack of attention to an important region of the image during the search. In this paper we discuss the use of eye tracker technology, in combination with image analysis and machine learning techniques, to learn what types of features catch the eye experienced radiologists when reading chest X-rays for diagnostic purposes, and to then use that information to produce saliency maps that predict what regions of each image might be most interesting to radiologists. We found that, out of 13 popular features types that are widely extracted to characterize images, 4 are particularly useful for this task: (1) Localized Edge Orientation Histograms (2) Haar Wavelets, (3) Gabor Filters, and (4) Steerable Filters.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2010: Computer-Aided Diagnosis
PublisherSPIE
Volume7624
ISBN (Electronic)9780819480255
DOIs
StatePublished - 2010
EventMedical Imaging 2010: Computer-Aided Diagnosis - San Diego, United States
Duration: Feb 16 2010Feb 18 2010

Other

OtherMedical Imaging 2010: Computer-Aided Diagnosis
CountryUnited States
CitySan Diego
Period2/16/102/18/10

Keywords

  • chest X-rays
  • eye tracking
  • feature extraction
  • machine learning
  • radiology experience
  • radiology training
  • saliency map
  • Saliency prediction

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Alzubaidi, M., Balasubramanian, V., Patel, A., Panchanathan, S., & Black, J. A. (2010). What catches a radiologist's eye? A comprehensive comparison of feature types for saliency prediction. In Medical Imaging 2010: Computer-Aided Diagnosis (Vol. 7624). [76240W] SPIE. https://doi.org/10.1117/12.844508