Interactive graph cut based segmentation with shape priors

Interactive or semi-automatic segmentation is a useful alternative to pure automatic segmentation in many applications. While automatic segmentation can be very challenging, a small amount of user input can often resolve ambiguous decisions on the part of the algorithm. In this work, we devise a graph cut algorithm for interactive segmentation which incorporates shape priors. While traditional graph cut approaches to interactive segmentation are often quite successful, they may fail in cases where there are diffuse edges, or multiple similar objects in close proximity to one another. Incorporation of shape priors within this framework mitigates these problems. Positive results on both medical and natural images are demonstrated.

[1]  Timothy F. Cootes,et al.  Statistical models of appearance for medical image analysis and computer vision , 2001, SPIE Medical Imaging.

[2]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[3]  Martin Styner,et al.  Automatic and Robust Computation of 3D Medial Models Incorporating Object Variability , 2003, International Journal of Computer Vision.

[4]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.

[5]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Guido Gerig,et al.  Multiscale medial shape-based analysis of image objects , 2003, Proc. IEEE.

[7]  Daniel Cremers,et al.  Nonlinear Shape Statistics in Mumford-Shah Based Segmentation , 2002, ECCV.

[8]  Christopher J. Taylor,et al.  Model-Based Interpretation of 3D Medical Images , 1993, BMVC.

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

[10]  Tao Zhang,et al.  Tracking objects using density matching and shape priors , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

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

[12]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[13]  Marie-Pierre Jolly,et al.  Interactive Organ Segmentation Using Graph Cuts , 2000, MICCAI.

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

[15]  Dinggang Shen,et al.  An Adaptive-Focus Deformable Model Using Statistical and Geometric Information , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[17]  Ingemar J. Cox,et al.  "Ratio regions": a technique for image segmentation , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[18]  K. Mardia,et al.  Statistical Shape Analysis , 1998 .

[19]  Ian H. Jermyn,et al.  Globally optimal regions and boundaries , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[20]  W. Eric L. Grimson,et al.  Coupled Multi-shape Model and Mutual Information for Medical Image Segmentation , 2003, IPMI.

[21]  Lawrence H. Staib,et al.  Integrated approaches to non-rigid registration in medical images , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[22]  Marko Subasic,et al.  Level Set Methods and Fast Marching Methods , 2003 .

[23]  Richard M. Leahy,et al.  An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[25]  Alan L. Yuille,et al.  Deformable templates , 1993 .