title: Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas.
abstract: BACKGROUND
  We sought to evaluate a new regional segmentation method for use with three-dimensional (3D) non-contrast abdominal CT images and to report the preliminary results.
  
  
  METHODS
  The proposed method was evaluated in ten cases. Manually segmented areas were used as the gold standard for evaluation. To compare the standard and the extracted liver regions, the degree of coincidence R% was redefined by transforming a volumetric overlap error. We also evaluated the influence of varying the density window size in terms of setting the starting points.
  
  
  RESULTS
  We confirmed in ten cases that our method could segment the liver region more precisely than the conventional method. A size of window 15 voxels was optimal as the starting point in all cases.
  
  
  CONCLUSIONS
  We demonstrated the accuracy of a 3D semiautomatic liver segmentation method for non-contrast CT. This method promises to offer radiologists a time-efficient segmentation aid.
authors.affiliation: Department of Biomaterials Science, Osaka University Graduate School of Dentistry, 1-8 Yamadaoka, Suita, Osaka 565-0871, Japan. Electronic address: yamagu@dent.osaka-u.ac.jp.
authors.forename: Satoshi
authors.initials: S
authors.lastname: Yamaguchi
authors.affiliation: College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan.
authors.forename: Koji
authors.initials: K
authors.lastname: Satake
authors.affiliation: College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan.
authors.forename: Yoshio
authors.initials: Y
authors.lastname: Yamaji
authors.affiliation: College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan.
authors.forename: Yen-Wei
authors.initials: YW
authors.lastname: Chen
authors.affiliation: College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan.
authors.forename: Hiromi T
authors.initials: HT
authors.lastname: Tanaka
mesh_terms: D000328:Adult; D000368:Aged; D016208:Databases, Factual; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Computed tomography; Liver segmentation; Liver/AH; Probabilistic models; Region-growing methodology