title: A deformable model for automatic CT liver extraction.
abstract: OBJECTIVE
  This study was performed to design an automatic liver region extraction system to facilitate clinical liver size estimation and further serve as a prestage for liver reconstruction and volume estimation.
  
  
  METHODS
  We present a modification of the well-known snakes algorithm for extracting liver regions in noisy CT images. Our modification addresses the issues of selection of the control points on an estimate of the contour and the determination of the weighting coefficients. The weighting coefficients are determined dynamically on the basis of the distance between the control points and the local curvature of the contour.
  
  
  RESULTS
  The proposed method was used in extracting liver regions from 98 cross-sectional abdominal images. The overall performance was estimated by comparisons with original liver regions.
  
  
  CONCLUSIONS
  The deformable model method enables an efficient and effective automatic liver region extraction in noisy environments. This approach eliminates human-in-the loop, which is the common practice for the majority of current methods.
authors.affiliation: Computer Science and Engineering Department, University of Texas, Arlington, TX 76019, USA. gao@cse.uta.edu
authors.forename: Jean
authors.initials: J
authors.lastname: Gao
authors.forename: Akio
authors.initials: A
authors.lastname: Kosaka
authors.forename: Avinash
authors.initials: A
authors.lastname: Kak
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008099:Liver / Q000000981:diagnostic imaging*; D036542:Tomography, Spiral Computed