A boundary extraction approach based on multi-resolution methods and the T-Snakes framework

We present a new approach which integrates the T-Snakes model and a multi-resolution method in a unified framework for segmentation and boundary extraction. In a first stage, a local scale property of the objects is used to define a triangulation of the image domain and a sampling (coarsest resolution) of the image field. The low resolution image is thresholded to get a 0-1 field which is processed by a simple continuation method to generate polygonal curves whose interior contain the desired objects. If the polygonal curve involves more than one object, then the resolution is increased in that region and the method will be applied again. This stage gives a rough approximation of the desired boundaries which will be improved by the T-Snakes to get the final result. We demonstrate the method for 2D medical imaging in the experimental results and indicate how it can be extended to 3D in future work.

[1]  Onçalves,et al.  Dual Topologically Adaptable Snakes , 2000 .

[2]  M. Berger,et al.  Adaptive mesh refinement for hyperbolic partial differential equations , 1982 .

[3]  Demetri Terzopoulos,et al.  Topologically adaptable snakes , 1995, Proceedings of IEEE International Conference on Computer Vision.

[4]  Azriel Rosenfeld,et al.  Segmentation and Estimation of Image Region Properties through Cooperative Hierarchial Computation , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Jean-Michel Jolion,et al.  The adaptive pyramid: A framework for 2D image analysis , 1991, CVGIP Image Underst..

[6]  R. Malladi,et al.  A unified geometric model for 3D confocal image analysis in cytology , 1998, Proceedings SIBGRAPI'98. International Symposium on Computer Graphics, Image Processing, and Vision (Cat. No.98EX237).

[7]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Demetri Terzopoulos,et al.  Image Analysis Using Multigrid Relaxation Methods , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Deborah Silver,et al.  Visualizing features and tracking their evolution , 1994, Computer.

[10]  P. Pérez,et al.  Multiscale minimization of global energy functions in some visual recovery problems , 1994 .

[11]  Thomas Ertl,et al.  Progressive Iso‐Surface Extraction from Hierarchical 3D Meshes , 1998, Comput. Graph. Forum.

[12]  Dimitris N. Metaxas,et al.  Image segmentation based on the integration of pixel affinity and deformable models , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[13]  Demetri Terzopoulos,et al.  Topology adaptive deformable surfaces for medical image volume segmentation , 1999, IEEE Transactions on Medical Imaging.

[14]  E. Allgower,et al.  Numerical Continuation Methods , 1990 .

[15]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

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

[17]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid , 2012 .

[18]  Azriel Rosenfeld,et al.  Digital Geometry: Introduction and Bibliography , 1997 .

[19]  Demetri Terzopoulos,et al.  Topologically adaptable deformable models for medical image analysis , 1997 .

[20]  Eugene L. Allgower,et al.  Numerical continuation methods - an introduction , 1990, Springer series in computational mathematics.

[21]  Supun Samarasekera,et al.  Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation , 1996, CVGIP Graph. Model. Image Process..

[22]  Max A. Viergever,et al.  Geodesic deformable models for medical image analysis , 1998, IEEE Transactions on Medical Imaging.

[23]  Brian C. Lovell,et al.  A Water Immersion Algorithm for Cytological Image Segmentation , 1996 .