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
Link To Document :
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