A Spatio-Temporal Modeling Method for Shape Representation

The spherical harmonic (SPHARM) description is a powerful surface modeling technique that can model arbitrarily shaped but simply connected three dimensional (3D) objects. Because SPHARM based 3D models can derive functional information analysis and classify different pathological symptoms, it has been used in many applications in biomedical image computing. There is an urgent requirement for efficient spatio-temporal shape modeling to represent the dynamic anatomical structures in many applications (e.g., medical image analysis, geospatial information systems). In this paper we propose a novel real spherical harmonics based spatio-temporal shape modeling method to efficiently and flexibly represent the shapes sequence of anatomical structures in medical images. Our method works well on the simply connected 3D objects and the effectiveness of our approach is demonstrated through theoretic and experimental exploration of a set of medical image applications. Furthermore, an evaluation criterion for spatio-temporal shape modeling efficiency is proposed and the comparison results showed the good performance of our method.

[1]  Ghassan Hamarneh,et al.  Deformable Spatio-Temporal Shape Models: Extending ASM to 2D+Time , 2001, BMVC.

[2]  Martin Styner,et al.  Shape versus Size: Improved Understanding of the Morphology of Brain Structures , 2001, MICCAI.

[3]  Jules Bloomenthal,et al.  Skeletal methods of shape manipulation , 1999, Proceedings Shape Modeling International '99. International Conference on Shape Modeling and Applications.

[4]  W. Eric L. Grimson,et al.  Small Sample Size Learning for Shape Analysis of Anatomical Structures , 2000, MICCAI.

[5]  Andrew J. Hanson,et al.  Hyperquadrics: Smoothly deformable shapes with convex polyhedral bounds , 1988, Comput. Vis. Graph. Image Process..

[6]  Dmitry B. Goldgof,et al.  The Use of Three- and Four-Dimensional Surface Harmonics for Rigid and Nonrigid Shape Recovery and Representation , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Graham J. L. Kemp,et al.  Fast computation, rotation, and comparison of low resolution spherical harmonic molecular surfaces , 1999, J. Comput. Chem..

[8]  Fred L. Bookstein,et al.  Landmark methods for forms without landmarks: morphometrics of group differences in outline shape , 1997, Medical Image Anal..

[9]  Ghassan Hamarneh,et al.  Deformable spatio-temporal shape models: extending active shape models to 2D+time , 2004, Image Vis. Comput..

[10]  O. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2002, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[11]  Takeo Kanade,et al.  Image-based spatio-temporal modeling and view interpolation of dynamic events , 2005, TOGS.

[12]  Thomas S. Huang,et al.  Modeling, Analysis, and Visualization of Left Ventricle Shape and Motion by Hierarchical Decomposition , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Dimitris N. Metaxas,et al.  A Finite Element Model for Functional Analysis of 4D Cardiac-Tagged MR Images , 2003, MICCAI.

[14]  Sven Loncaric,et al.  A survey of shape analysis techniques , 1998, Pattern Recognit..

[15]  Ruzena Bajcsy,et al.  Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Y. Aloimonos,et al.  Spatio-Temporal Stereo Using Multi-Resolution Subdivision Surfaces , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[17]  Gilles Burel,et al.  Determination of the Orientation of 3D Objects Using Spherical Harmonics , 1995, CVGIP Graph. Model. Image Process..

[18]  Martin Styner,et al.  Three-dimensional medial shape representation incorporating object variability , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[19]  Mark A. Ganter,et al.  Skeleton-based modeling operations on solids , 1997, SMA '97.

[20]  Fillia Makedon,et al.  A Prediction Framework for Cardiac Resynchronization Therapy Via 4D Cardiac Motion Analysis , 2005, MICCAI.

[21]  Fillia Makedon,et al.  Surface Alignment of 3D Spherical Harmonic Models: Application to Cardiac MRI Analysis , 2005, MICCAI.

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

[23]  Dimitris N. Metaxas,et al.  Dynamic 3D models with local and global deformations: deformable superquadrics , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[24]  Örjan Smedby,et al.  Compact and efficient 3D shape description through radial function approximation , 2003, Comput. Methods Programs Biomed..