Fully deformable 3D digital partition model with topological control

We propose a purely discrete deformable partition model for segmenting 3D images. Its main ability is to maintain the topology of the partition during the minimization process. To do so, our main contribution is a new definition of multi-label simple points (ML simple point) that is easily computable. An ML simple point can be relabeled without modifying the overall topology of the partition. The definition is based on intervoxel properties, and uses the notion of collapse on cubical complexes. This work is an extension of a former restricted definition (Dupas et al., 2009) that prohibits the move of intersections of boundary surfaces. A deformation process is carried out with a greedy energy minimization algorithm. A discrete area estimator is used to approach at best standard regularizers classically used in continuous energy minimizing methods. We illustrate the potential of our approach with the segmentation of 3D medical images with known expected topology.

[1]  Jacques-Olivier Lachaud,et al.  Multi-Label Simple Points Definition for 3D Images Digital Deformable Model , 2009, DGCI.

[2]  Gilles Bertrand,et al.  New Characterizations of Simple Points, Minimal Non-simple Sets and P-Simple Points in 2D, 3D and 4D Discrete Spaces , 2008, DGCI.

[3]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[4]  Vladimir Kolmogorov,et al.  Computing geodesics and minimal surfaces via graph cuts , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[5]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[6]  Hervé Le Men,et al.  Scale-Sets Image Analysis , 2005, International Journal of Computer Vision.

[7]  Pierre-Louis Bazin,et al.  Digital Homeomorphisms in Deformable Registration , 2007, IPMI.

[8]  Jacques-Olivier Lachaud,et al.  Geometric Measures on Arbitrary Dimensional Digital Surfaces , 2003, DGCI.

[9]  Yvan G. Leclerc,et al.  Constructing simple stable descriptions for image partitioning , 1989, International Journal of Computer Vision.

[10]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[11]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Donald Geman,et al.  Gibbs distributions and the bayesian restoration of images , 1984 .

[14]  Jacques-Olivier Lachaud,et al.  Espaces non-euclidiens et analyse d'image : modèles déformables riemanniens et discrets, topologie et géométrie discrète. (Non-Euclidean spaces and image analysis : Riemannian and discrete deformable models, discrete topology and geometry) , 2006 .

[15]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Daniel Cremers,et al.  A convex relaxation approach for computing minimal partitions , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Stuart German,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1988 .

[18]  Luc Brun,et al.  Hierarchy Construction Schemes Within the Scale Set Framework , 2007, GbRPR.

[19]  Guillermo Sapiro,et al.  Minimal Surfaces Based Object Segmentation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Guillaume Damiand,et al.  First Results for 3D Image Segmentation with Topological Map , 2008, DGCI.

[21]  Jacques-Olivier Lachaud,et al.  Discrete Deformable Boundaries for the Segmentation of Multidimensional Images , 2001, IWVF.

[22]  François de Vieilleville,et al.  Fast, accurate and convergent tangent estimation on digital contours , 2007, Image Vis. Comput..

[23]  Gabor T. Herman,et al.  Geometry of digital spaces , 1998, Optics & Photonics.

[24]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Daniel Cremers,et al.  A convex approach for computing minimal partitions , 2008 .

[26]  Jean-Philippe Pons,et al.  Delaunay Deformable Models: Topology-Adaptive Meshes Based on the Restricted Delaunay Triangulation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Alan C. Evans,et al.  BrainWeb: Online Interface to a 3D MRI Simulated Brain Database , 1997 .

[28]  Florent Ségonne,et al.  Active Contours Under Topology Control—Genus Preserving Level Sets , 2008, International Journal of Computer Vision.

[29]  Laurent D. Cohen,et al.  Global Minimum for Active Contour Models: A Minimal Path Approach , 1997, International Journal of Computer Vision.

[30]  Gilles Bertrand,et al.  Simple points, topological numbers and geodesic neighborhoods in cubic grids , 1994, Pattern Recognit. Lett..

[31]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[32]  François de Vieilleville,et al.  Digital Deformable Model Simulating Active Contours , 2009, DGCI.

[33]  Oscar Firschein,et al.  Readings in computer vision: issues, problems, principles, and paradigms , 1987 .

[34]  Laurent D. Cohen,et al.  Fast Constrained Surface Extraction by Minimal Paths , 2006, International Journal of Computer Vision.

[35]  D. Greig,et al.  Exact Maximum A Posteriori Estimation for Binary Images , 1989 .

[36]  Vladimir A. Kovalevsky,et al.  Finite topology as applied to image analysis , 1989, Comput. Vis. Graph. Image Process..

[37]  Xiao Han,et al.  A Topology Preserving Level Set Method for Geometric Deformable Models , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.