title: Fractal analysis of visual search activity for mass detection during mammographic screening.
abstract: OBJECTIVE
  The objective of this study was to assess the complexity of human visual search activity during mammographic screening using fractal analysis and to investigate its relationship with case and reader characteristics.
  
  
  METHODS
  The study was performed for the task of mammographic screening with simultaneous viewing of four coordinated breast views as typically done in clinical practice. Eye-tracking data and diagnostic decisions collected for 100 mammographic cases (25 normal, 25 benign, 50 malignant) from 10 readers (three board certified radiologists and seven Radiology residents), formed the corpus for this study. The fractal dimension of the readers' visual scanning pattern was computed with the Minkowski-Bouligand box-counting method and used as a measure of gaze complexity. Individual factor and group-based interaction ANOVA analysis was performed to study the association between fractal dimension, case pathology, breast density, and reader experience level. The consistency of the observed trends depending on gaze data representation was also examined.
  
  
  RESULTS
  Case pathology, breast density, reader experience level, and individual reader differences are all independent predictors of the complexity of visual scanning pattern when screening for breast cancer. No higher order effects were found to be significant.
  
  
  CONCLUSIONS
  Fractal characterization of visual search behavior during mammographic screening is dependent on case properties and image reader characteristics.
authors.affiliation: Biomedical Sciences, Engineering, and Computing Group, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
authors.forename: Folami
authors.initials: F
authors.lastname: Alamudun
authors.affiliation: Biomedical Sciences, Engineering, and Computing Group, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
authors.forename: Hong-Jun
authors.initials: HJ
authors.lastname: Yoon
authors.affiliation: Department of Radiology, University of Tennessee Medical Center at Knoxville, Knoxville, TN, 37920, USA.
authors.forename: Kathleen B
authors.initials: KB
authors.lastname: Hudson
authors.affiliation: Department of Radiology, University of Tennessee Medical Center at Knoxville, Knoxville, TN, 37920, USA.
authors.forename: Garnetta
authors.initials: G
authors.lastname: Morin-Ducote
authors.affiliation: Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA, 77843.
authors.forename: Tracy
authors.initials: T
authors.lastname: Hammond
authors.affiliation: Biomedical Sciences, Engineering, and Computing Group, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
authors.forename: Georgia D
authors.initials: GD
authors.lastname: Tourassi
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003951:Diagnostic Errors; D053483:Eye Movement Measurements; D005133:Eye Movements; D005260:Female; D017709:Fractals; D006801:Humans; D007396:Internship and Residency; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D011361:Professional Competence; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D000072177:Radiologists; D014796:Visual Perception
keywords: diagnostic radiology error; eye tracking; fractal analysis; mammography; visual search