Combined Boundary-Medial Shape Description of Variable Biological Objects

This dissertation describes a novel shape description scheme that incorporates variability of an object population into the generation of a characteristic 3D shape model. Knowledge about the biological variability of anatomical objects is essential for statistical shape analysis and discrimination between healthy and pathological structures. The proposed shape representation is based on a fine-scale spherical harmonics (SPHARM) description and a coarse-scale m-rep description. The SPHARM description describes the boundary as a weighted series of spherical harmonics. The correspondence on the boundary is defined by a first-order ellipsoid normalized parameterization. The medial m-rep description is composed of a net of medial primitives with fixed graph properties. A m-rep model is computed automatically from the shape space of a training population of SPHARM objects. Pruned 3D Voronoi skeletons are used to determine a common medial branching topology in a stable way. An intrinsic coordinate system and an implicit correspondence between objects are defined on the medial manifold. My novel representation scheme describes shape and shape changes in a meaningful and intuitive manner. Several experimental studies of shape asymmetry and shape similarity in biological structures demonstrate the power of the new representation to describe global and local form. The clinical importance of shape measurements is shown in the presented applications. The contributions made in this dissertation include the development of a novel automatic pruning scheme for 3D Voronoi skeletons. My experiments showed that only a small number of skeletal sheets are necessary to describe families of even quite complex objects. This work is also the first to compute a common medial branching topology of an object population, which deals with the sensitivity of the branching topology to small shape variations. The sensitivity of the medial descriptions to small boundary perturbations, a fundamental problem of any skeletonization technique, is approached with a new sampling technique.

[1]  C. A. Burbeck,et al.  Linking object boundaries at scale: a common mechanism for size and shape judgments , 1996, Vision Research.

[2]  W. Eric L. Grimson,et al.  Fixed topology skeletons , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[3]  James S. Duncan,et al.  Boundary Finding with Parametrically Deformable Models , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  G. Christensen,et al.  Large Deformation Fluid Diffeomorphisms for Landmark and Image Matching , 1999 .

[5]  F. Bookstein Landmark methods for forms without landmarks: localizing group differences in outline shape , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[6]  Benjamin B. Kimia,et al.  A formal classification of 3D medial axis points and their local geometry , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Jerry L Prince,et al.  A computerized approach for morphological analysis of the corpus callosum. , 1996, Journal of computer assisted tomography.

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

[10]  Xinhua Zhuang,et al.  Image Analysis Using Mathematical Morphology , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[13]  Marshall W. Bern,et al.  A new Voronoi-based surface reconstruction algorithm , 1998, SIGGRAPH.

[14]  W. Eric L. Grimson,et al.  Statistical Shape Analysis Using Fixed Topology Skeletons: Corpus Callosum Study , 1999, IPMI.

[15]  Michael I. Miller,et al.  On The Geometry and Shape of Brain Sub-Manifolds , 1997, Int. J. Pattern Recognit. Artif. Intell..

[16]  R. Rabbitt,et al.  3D brain mapping using a deformable neuroanatomy. , 1994, Physics in medicine and biology.

[17]  J. Ehrhardt,et al.  Regional brain abnormalities in schizophrenia measured with magnetic resonance imaging. , 1994, JAMA.

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

[19]  Kaleem Siddiqi,et al.  Ligature instabilities in the perceptual organization of shape , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[20]  V. Ralph Algazi,et al.  Continuous skeleton computation by Voronoi diagram , 1991, CVGIP Image Underst..

[21]  K. Rohr,et al.  Thin-plate spline approximation for image registration , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Kaleem Siddiqi,et al.  Shapes, shocks and wiggles , 1999, Image Vis. Comput..

[23]  Timothy F. Cootes,et al.  A Unified Framework for Atlas Matching Using Active Appearance Models , 1999, IPMI.

[24]  Steven W. Zucker,et al.  On the evolution of the skeleton , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[25]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[26]  Frank Y. Shih,et al.  A skeletonization algorithm by maxima tracking on Euclidean distance transform , 1995, Pattern Recognit..

[27]  Christos Davatzikos,et al.  Spatial Transformation and Registration of Brain Images Using Elastically Deformable Models , 1997, Comput. Vis. Image Underst..

[28]  Fred L. Bookstein,et al.  How to produce a landmark point: the statistical geometry of incompletely registered images , 1995, Optics & Photonics.

[29]  Hemant D. Tagare,et al.  A geometric criterion for shape-based non-rigid correspondence , 1995, Proceedings of IEEE International Conference on Computer Vision.

[30]  D'arcy W. Thompson,et al.  On Growth and Form , 1917, Nature.

[31]  Kaleem Siddiqi,et al.  Hyperbolic "Smoothing" of shapes , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[32]  U. Grenander,et al.  Hippocampal morphometry in schizophrenia by high dimensional brain mapping. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[33]  D. Weinberger,et al.  Genetic variability of human brain size and cortical gyral patterns. , 1997, Brain : a journal of neurology.

[34]  Kaleem Siddiqi,et al.  The Hamilton-Jacobi skeleton , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[35]  Christopher J. Taylor,et al.  3D point distribution models of the cortical sulci , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[36]  Guido Gerig,et al.  Medial models incorporating shape variability for 3 D shape analysis , 2000 .

[37]  Guido Gerig,et al.  Segmentation of 2-D and 3-D objects from MRI volume data using constrained elastic deformations of flexible Fourier contour and surface models , 1996, Medical Image Anal..

[38]  Anand Rangarajan,et al.  The Softassign Procrustes Matching Algorithm , 1997, IPMI.

[39]  James S. Duncan,et al.  Model-based deformable surface finding for medical images , 1996, IEEE Trans. Medical Imaging.

[40]  R. McCarley,et al.  MRI anatomy of schizophrenia , 1999, Biological Psychiatry.

[41]  Nicholas Ayache,et al.  A scheme for automatically building three-dimensional morphometric anatomical atlases: application to a skull atlas , 1998, Medical Image Anal..

[42]  Christian Michael Brechbühler Description and analysis of 3-D shapes by parametrization of closed surfaces , 1995 .

[43]  William H. Press,et al.  Numerical recipes in C , 2002 .

[44]  Benjamin B. Kimialems,et al.  Curve Evolution, Wave Propagation, and Mathematical Morphology , 1998 .

[45]  Hemant D. Tagare,et al.  Non-rigid Curve Correspondence for Estimating Heart Motion , 1997, IPMI.

[46]  P. Thomas Fletcher,et al.  Multi-scale 3-D Deformable Model Segmentation Based on Medial Description , 2001, IPMI.

[47]  Christopher J. Taylor,et al.  Automatic Construction of Eigenshape Models by Genetic Algorithm , 1997, IPMI.

[48]  C. Taylor,et al.  Active shape models - 'Smart Snakes'. , 1992 .

[49]  L. DeLisi,et al.  A prospective follow-up study of brain morphology and cognition in first-episode schizophrenic patients: Preliminary findings , 1995, Biological Psychiatry.

[50]  Dominique Attali,et al.  Computing and Simplifying 2D and 3D Continuous Skeletons , 1997, Comput. Vis. Image Underst..

[51]  Ching Y. Suen,et al.  Thinning Methodologies - A Comprehensive Survey , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  D'arcy W. Thompson On growth and form i , 1943 .

[53]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[54]  Paul A. Yushkevich,et al.  Segmentation, registration, and measurement of shape variation via image object shape , 1999, IEEE Transactions on Medical Imaging.

[55]  Stephen M. Pizer,et al.  Shape Modeling and Image Visualization in 3D with M-rep Object Models , 2002 .

[56]  M. Styner,et al.  Hybrid boundary-medial shape description for biologically variable shapes , 2000, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737).

[57]  R. Bajcsy,et al.  Three dimensional object representation revisited , 1987 .

[58]  King-Sun Fu,et al.  A parallel thinning algorithm for 3-D pictures , 1981 .