• DocumentCode
    1105356
  • Title

    Shape Registration by Optimally Coding Shapes

  • Author

    Jiang, Yifeng ; Xie, Jun ; Tsui, Hung-Tat

  • Author_Institution
    Dept. of Diagnostic Radiol., Yale Univ., New Haven, CT
  • Volume
    12
  • Issue
    5
  • fYear
    2008
  • Firstpage
    627
  • Lastpage
    635
  • Abstract
    This paper formulates shape registration as an optimal coding problem. It employs a set of landmarks to establish the correspondence between shapes, and assumes that the best correspondence can be achieved when the polygons formed by the landmarks optimally code all the shape contours, i.e., obtain their minimum description length (MDL). This is different from previous MDL-based shape registration methods, which code the landmark locations. In this paper, each contour is discretized to be a set of points to make the coding feasible, and a number of strategies are adopted to tackle the difficult optimization problem involved. The resulting algorithm, called CAP, is able to yield statistical shape model with better quality in terms of model generalization error, which is demonstrated on both synthetic and biomedical shapes.
  • Keywords
    encoding; shape measurement; minimum description length; optimally coding shapes; shape registration; Minimum Description Length (MDL) principle; Minimum description length (MDL) principle; Point Distribution Model (PDM); Shape registration; point distribution model (PDM); shape registration; statistical shape model; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
  • fLanguage
    English
  • Journal_Title
    Information Technology in Biomedicine, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1089-7771
  • Type

    jour

  • DOI
    10.1109/TITB.2008.920798
  • Filename
    4472914