Level set based segmentation with intensity and curvature priors

A method is presented for segmentation of anatomical structures that incorporates prior information about the intensity and curvature profile of the structure from a training set of images and boundaries. Specifically, we model the intensity distribution as a function of signed distance from the object boundary, instead of modeling only the intensity of the object as a whole. A curvature profile acts as a boundary regularization term specific to the shape being extracted, as opposed to simply penalizing high curvature. Using the prior model, the segmentation process estimates a maximum a posteriori higher dimensional surface whose zero level set converges on the boundary of the object to be segmented. Segmentation results are demonstrated on synthetic data and magnetic resonance imagery.

[1]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[2]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[3]  Guido Gerig,et al.  Unsupervised tissue type segmentation of 3D dual-echo MR head data , 1992, Image Vis. Comput..

[4]  Anthony J. Yezzi,et al.  Gradient flows and geometric active contour models , 1995, Proceedings of IEEE International Conference on Computer Vision.

[5]  Benjamin B. Kimia,et al.  Image segmentation by reaction-diffusion bubbles , 1995, Proceedings of IEEE International Conference on Computer Vision.

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

[7]  H. Soner,et al.  Level set approach to mean curvature flow in arbitrary codimension , 1996 .

[8]  Guillermo Sapiro,et al.  Vector-valued active contours , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Ron Kikinis,et al.  Markov random field segmentation of brain MR images , 1997, IEEE Transactions on Medical Imaging.

[10]  Kaleem Siddiqi,et al.  Area and length minimizing flows for shape segmentation , 1998, IEEE Trans. Image Process..

[11]  R. Deriche,et al.  Geodesic Active Regions for Texture Segmentation , 1998 .

[12]  Olivier D. Faugeras,et al.  Co-dimension 2 Geodesic Active Contours for MRA Segmentation , 1999, IPMI.

[13]  Anthony J. Yezzi,et al.  A statistical approach to snakes for bimodal and trimodal imagery , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Tina Kapur,et al.  Model based three dimensional medical image segmentation , 1999 .

[15]  Olivier D. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[16]  Stanley Osher,et al.  Level Set Methods , 2003 .

[17]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[18]  William T. Freeman,et al.  Learning Low-Level Vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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