Title :
Automatic Construction of Correspondences for Tubular Surfaces
Author :
Huysmans, Toon ; Sijbers, Jan ; Brigitte, V.
Author_Institution :
Dept. of Phys., Univ. of Antwerp, Antwerp, Belgium
fDate :
4/1/2010 12:00:00 AM
Abstract :
Statistical shape modeling is an established technique and is used for a variety of tasks in medical image processing, such as image segmentation and analysis. A challenging task in the construction of a shape model is establishing a good correspondence across the set of training shapes. Especially for shapes of cylindrical topology, very little work has been done. This paper describes an automatic method to obtain a correspondence for a set of cylindrical shapes. The method starts from an initial correspondence which is provided by cylindrical parameterization. The quality of the obtained correspondence, measured in terms of the description length, is then improved by deforming the parameterizations using cylindrical b-spline deformations and by optimization of the spatial alignment of the shapes. In order to allow efficient gradient-guided optimization, an analytic expression is provided for the gradient of this quality measure with respect to the parameters of the parameterization deformation and the spatial alignment. A comparison is made between models obtained from the correspondences before and after the optimization. The results show that, in comparison with parameterization-based correspondences, this new method establishes correspondences that generate models with significantly increased performance in terms of reconstruction error, generalization ability, and specificity.
Keywords :
gradient methods; image reconstruction; image segmentation; medical image processing; optimisation; statistical analysis; topology; automatic construction; cylindrical b-spline deformations; cylindrical parameterization; cylindrical topology; generalization ability; gradient-guided optimization; image segmentation; medical image processing; parameterization-based correspondences; reconstruction error; statistical shape modeling; tubular surfaces; Point correspondence problem; image segmentation; image shape analysis.; minimum description length; statistical shape models; tubular structures; Algorithms; Diagnostic Imaging; Humans; Image Processing, Computer-Assisted; Least-Squares Analysis; Models, Statistical; Normal Distribution; Pattern Recognition, Automated; Phantoms, Imaging; Principal Component Analysis;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2009.93