title: Comparison of 3-point Dixon imaging and fuzzy C-means clustering methods for breast density measurement.
abstract: OBJECTIVE
  To assess two methods of fat and fibroglandular tissue (FGT) segmentation for measuring breast MRI FGT volume and FGT%, the volume percentage of FGT in the breast, in longitudinal studies.
  
  
  METHODS
  Nine premenopausal women provided one MRI per week for 4 weeks during a natural menstrual cycle for a total of 36 datasets. We compared a fuzzy c-means (FC) and a 3-point Dixon segmentation method for estimation of changes in FGT volume and FGT% across the menstrual cycle. We also assessed whether differences due to changes in positioning each week could be minimized by coregistration, i.e., the application of the breast boundary selected at one visit to images obtained at other visits.
  
  
  RESULTS
  FC and Dixon FGT volume were highly correlated (r = 0.93, P < 0.001), as was FC and Dixon FGT% (r = 0.86, P = 0.01), although Dixon measurements were on average 10-20% higher. Although FGT measured by both methods showed the expected pattern of increase during the menstrual cycle, the magnitude, and for one woman the direction, of change varied according to the method used. Measurements of FGT for coregistered images were in close agreement with those for which the boundaries were determined independently.
  
  
  CONCLUSIONS
  The method of segmentation of fat and FGT tissue may have an impact on the results of longitudinal studies of changes in breast MRI FGT.
authors.affiliation: New York University School of Medicine, Department of Environmental Medicine, Division of Epidemiology, New York, NY 10016, USA. tess.clendenen@nyumc.org
authors.forename: Tess V
authors.initials: TV
authors.lastname: Clendenen
authors.forename: Anne
authors.initials: A
authors.lastname: Zeleniuch-Jacquotte
authors.forename: Linda
authors.initials: L
authors.lastname: Moy
authors.forename: Malcolm C
authors.initials: MC
authors.lastname: Pike
authors.forename: Henry
authors.initials: H
authors.lastname: Rusinek
authors.forename: Sungheon
authors.initials: S
authors.lastname: Kim
mesh_terms: D000328:Adult; D000465:Algorithms; D001940:Breast / Q000033:anatomy & histology* / Q000502:physiology*; D003720:Densitometry / Q000379:methods*; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult
keywords: 3-point Dixon; breast MRI; fibroglandular tissue; fibroglandular tissue %, breast density; fuzzy c-means; segmentation