Interactive Organ Segmentation Using Graph Cuts

An N-dimensional image is divided into “object” and “background” segments using a graph cut approach. A graph is formed by connecting all pairs of neighboring image pixels (voxels) by weighted edges. Certain pixels (voxels) have to be a priori identified as object or background seeds providing necessary clues about the image content. Our objective is to find the cheapest way to cut the edges in the graph so that the object seeds are completely separated from the background seeds. If the edge cost is a decreasing function of the local intensity gradient then the minimum cost cut should produce an object/background segmentation with compact boundaries along the high intensity gradient values in the image. An efficient, globally optimal solution is possible via standard min-cut/max-flow algorithms for graphs with two terminals. We applied this technique to interactively segment organs in various 2D and 3D medical images.

[1]  Andrew V. Goldberg,et al.  A new approach to the maximum flow problem , 1986, STOC '86.

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

[3]  Ramesh C. Jain,et al.  Using Dynamic Programming for Solving Variational Problems in Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

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

[6]  Mubarak Shah,et al.  A Fast algorithm for active contours and curvature estimation , 1992, CVGIP Image Underst..

[7]  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..

[8]  Laurent D. Cohen,et al.  Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Colin Studholme,et al.  Hierarchical Segmentation Satisfying Constraints , 1994, BMVC.

[10]  H. S. Sawhney,et al.  Intelligent interactive image outlining using spline snakes , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[11]  Emanuele Trucco,et al.  Computer and Robot Vision , 1995 .

[12]  Alok Gupta,et al.  Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

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

[14]  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..

[15]  Guido Gerig,et al.  A user-guided tool for efficient segmentation of medical image data , 1997, CVRMed.

[16]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  William A. Barrett,et al.  Interactive Segmentation with Intelligent Scissors , 1998, Graph. Model. Image Process..

[18]  Davi Geiger,et al.  Segmentation by grouping junctions , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[19]  Olga Veksler,et al.  Markov random fields with efficient approximations , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[20]  Jayaram K. Udupa,et al.  User-Steered Image Segmentation Paradigms: Live Wire and Live Lane , 1998, Graph. Model. Image Process..

[21]  Alok Gupta,et al.  Optimal polyline tracking for artery motion compensation in coronary angiography , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[22]  Marie-Pierre Jolly,et al.  A cooperative framework for segmentation using 2D active contours and 3D hybrid models as applied to branching cylindrical structures , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

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