Automatic Construction of Correspondences for Tubular Surfaces

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.

[1]  Paramate Horkaew,et al.  Construction of 3D Dynamic Statistical Deformable Models for Complex Topological Shapes , 2004, MICCAI.

[2]  Hong Qin,et al.  Globally Optimal Surface Mapping for Surfaces with Arbitrary Topology , 2008, IEEE Transactions on Visualization and Computer Graphics.

[3]  Timothy F. Cootes,et al.  A minimum description length approach to statistical shape modeling , 2002, IEEE Transactions on Medical Imaging.

[4]  B. Barsky,et al.  An Introduction to Splines for Use in Computer Graphics and Geometric Modeling , 1987 .

[5]  Hans-Peter Meinzer,et al.  3D Active Shape Models Using Gradient Descent Optimization of Description Length , 2005, IPMI.

[6]  I. Jolliffe Principal Component Analysis , 2002 .

[7]  Christopher J. Taylor,et al.  Automatic construction of eigenshape models by direct optimization , 1998, Medical Image Anal..

[8]  Dirk Loeckx,et al.  Automated Nonrigid Intra-Patient Image Registration Using B-Splines (Automatische niet-rigide intra-patient beeldregistratie met behulp van B-splines) , 2006 .

[9]  Philipp Birken,et al.  Numerical Linear Algebra , 2011, Encyclopedia of Parallel Computing.

[10]  Timothy F. Cootes,et al.  Use of active shape models for locating structures in medical images , 1994, Image Vis. Comput..

[11]  Xianfeng Gu,et al.  Discrete Surface Ricci Flow , 2008, IEEE Transactions on Visualization and Computer Graphics.

[12]  Stefan Zachow,et al.  Reconstruction of mandibular dysplasia using a statistical 3D shape model , 2005 .

[13]  L. Trefethen,et al.  Numerical linear algebra , 1997 .

[14]  Martin Styner,et al.  Shape Modeling and Analysis with Entropy-Based Particle Systems , 2007, IPMI.

[15]  Guido Gerig,et al.  Parametrization of Closed Surfaces for 3-D Shape Description , 1995, Comput. Vis. Image Underst..

[16]  Manolis I. A. Lourakis,et al.  Estimating the Jacobian of the Singular Value Decomposition: Theory and Applications , 2000, ECCV.

[17]  Rasmus Reinhold Paulsen,et al.  Building and Testing a Statistical Shape Model of the Human Ear Canal , 2002, MICCAI.

[18]  Max A. Viergever,et al.  Interactive segmentation of abdominal aortic aneurysms in CTA images , 2004, Medical Image Anal..

[19]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[20]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[21]  Paramate Horkaew,et al.  Optimal Deformable Surface Models for 3D Medical Image Analysis , 2003, IPMI.

[22]  Ron Kikinis,et al.  Nondistorting flattening maps and the 3-D visualization of colon CT images , 2000, IEEE Transactions on Medical Imaging.

[23]  Martin Styner,et al.  Evaluation of 3D Correspondence Methods for Model Building , 2003, IPMI.

[24]  Mark A. van Buchem,et al.  GAMEs: Growing and adaptive meshes for fully automatic shape modeling and analysis , 2007, Medical Image Anal..

[25]  Douglas W. Jones,et al.  Morphometric analysis of lateral ventricles in schizophrenia and healthy controls regarding genetic and disease-specific factors. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Guido Gerig,et al.  Elastic model-based segmentation of 3-D neuroradiological data sets , 1999, IEEE Transactions on Medical Imaging.

[27]  Thomas Lange,et al.  A Statistical Shape Model for the Liver , 2002, MICCAI.

[28]  J.J.S.P. Cabral,et al.  An introduction to splines for use in computer graphics & geometric modeling: Richard H. Bartels, John C. Beatty & Brian A. Barsky, Morgan Kaufmann Publishers, Inc., Los Altos, California, 1987 , 1992 .

[29]  Hugues Hoppe,et al.  Spherical parametrization and remeshing , 2003, ACM Trans. Graph..

[30]  D. Kendall MORPHOMETRIC TOOLS FOR LANDMARK DATA: GEOMETRY AND BIOLOGY , 1994 .

[31]  Hans-Christian Hege,et al.  A 3D statistical shape model of the pelvic bone for segmentation , 2004, SPIE Medical Imaging.

[32]  Marleen de Bruijne,et al.  Adapting Active Shape Models for 3D Segmentation of Tubular Structures in Medical Images , 2003, IPMI.

[33]  Feng Qiu,et al.  Conformal virtual colon flattening , 2006, SPM '06.

[34]  E. Catmull,et al.  Recursively generated B-spline surfaces on arbitrary topological meshes , 1978 .

[35]  David A. Rottenberg,et al.  Quasi-Conformally Flat Mapping the Human Cerebellum , 1999, MICCAI.

[36]  Kalle Åström,et al.  Minimizing the description length using steepest descent , 2003, BMVC.

[37]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[38]  K. Mardia,et al.  Statistical Shape Analysis , 1998 .

[39]  Hans Henrik Thodberg,et al.  Minimum Description Length Shape and Appearance Models , 2003, IPMI.

[40]  Tony DeRose,et al.  Multiresolution analysis of arbitrary meshes , 1995, SIGGRAPH.

[41]  Michael S. Floater,et al.  Parametrization and smooth approximation of surface triangulations , 1997, Comput. Aided Geom. Des..

[42]  Alla Sheffer,et al.  Fundamentals of spherical parameterization for 3D meshes , 2003, ACM Trans. Graph..

[43]  T. Chan,et al.  Genus zero surface conformal mapping and its application to brain surface mapping. , 2004, IEEE transactions on medical imaging.

[44]  Jan Sijbers,et al.  Improved Shape Modeling of Tubular Objects Using Cylindrical Parameterization , 2006, MIAR.

[45]  Timothy F. Cootes,et al.  3D Statistical Shape Models Using Direct Optimisation of Description Length , 2002, ECCV.