Segmentation of Biological Volume Datasets Using a Level-Set Framework

  • Ross Whitaker
  • David Breen
  • Ken Museth
  • Neha Soni
Part of the Eurographics book series (EUROGRAPH)


This paper presents a framework for extracting surface models from a broad variety of volume datasets. These datasets are produced from standard 3D imaging devices, and are all noisy samplings of complex biological structures with boundaries that have low and often varying contrasts. The level set segmentation method, which is well documented in the literature, creates a new volume from the input data by solving an initial-value partial differential equation (PDE) with user-defined feature-extracting terms. However, level set deformations alone are not sufficient, they must be combined with powerful initialization techniques in order to produce successful segmentations. Our level set segmentation approach consists of defining a set of suitable pre-processing techniques for initialization and selecting/tuning different feature-extracting terms in the level set algorithm. This collection of techniques forms a toolkit that can be applied, under the guidance of a user, to segment a variety of volumetric data.


Deformable Model Gradient Magnitude Volume Dataset Constructive Solid Geometry Frog Embryo 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    L. Alvarez and J.-M. Morel. A morphological approach to multiscale analysis: From principles to equations. In Bart M. ter Haar Romeny, editor, Geometry-Driven Diffusion in Computer Vision, pages 4–21. Kluwer Academic Publishers, 1994.Google Scholar
  2. 2.
    D. Breen and R. Whitaker. A level set approach for the metamorphosis of solid models. IEEE Transactions on Visualization and Computer Graphics, 7 (2): 173–192, 2001.CrossRefGoogle Scholar
  3. 3.
    J.F. Canny. A computational approach to edge detection. IEEE Trans. on Pat. Anal. and Mach. Intel., 8 (6): 679–698, 1986.CrossRefGoogle Scholar
  4. 4.
    T. Cootes, A. Hill, C.J. Taylor, and J. Haslam. The use of active shape models for locating structures in medical images. In H. H. Barrett and A. F. Gmitro, editors, Information Processing in Medical Imaging (IPMI’93), number 687 in Lecture Notes in Computer Science, pages 33–47. Springer-Verlag, 1993.CrossRefGoogle Scholar
  5. 5.
    Robert A. Drebin, Loren Carpenter, and Pat Hanrahan. Volume rendering. In SIGGRAPH ‘88 Proceedings, pages 65–74, August 1988.CrossRefGoogle Scholar
  6. 6.
    R. Fedkiw, R. Aslam, R. Merriman, and S. Osher. A non-oscillatory eulerian approach to interfaces in multimaterial flows (the ghost fluid method). Journal of Computational Physics, 152: 457–492, 1999.MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    P. Getto and D. Breen. An object-oriented architecture for a computer animation system. The Visual Computer, 6 (2): 79–92, March 1990.CrossRefGoogle Scholar
  8. David H. Laidlaw, Kurt W. Fleischer, and Alan H. Barr. Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms. IEEE Transactions on Medical Imaging, 17(1):74–86, feb 1998.CrossRefGoogle Scholar
  9. 9.
    M. Leventon, O. Faugeraus, W. Grimson, and W. Wells III. Level set based segmentation with intensity and curvature priors. In Workshop on Mathematical Methods in Biomedical Image Analysis Proceedings, pages 4–11, June 2000.Google Scholar
  10. 10.
    Marc Levoy. Display of surfaces from volume data. IEEE Computer Graphics and Applications, 9 (3): 245–261, 1990.MATHGoogle Scholar
  11. 11.
    W.E. Lorenson and H.E. Cline. Marching Cubes: A high resolution 3D surface construction algorithm. Computer Graphics, 21 (4): 163–169, 1982.CrossRefGoogle Scholar
  12. 12.
    Ravikanth Malladi, James A. Sethian, and Baba C. Vemuri. Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17 (2): 158–175, 1995.CrossRefGoogle Scholar
  13. 13.
    D. Mart Vision. Freeman, San Francisco, 1982.Google Scholar
  14. 14.
    T. McInerney and D. Terzopoulos. A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4d image analysis. Computerized Medical Imaging and Graphics,19(1):69–83,1995.’CrossRefGoogle Scholar
  15. 15.
    J.V. Miller, D.E. Breen, W.E. Lorensen, R.M. O’Bara, and M.J. Wozny. Geometrically deformed models: A method for extracting closed geometric models from volume data. In SIGGRAPH ‘81 Proceedings, pages 217–226, July 1991.Google Scholar
  16. 16.
    Shigeru Muraki. Volumetric shape description of range data using “blobby model”. In Thomas W. Sederberg, editor, SIGGRAPH ‘81 Proceedings, pages 227–235, July 1991.Google Scholar
  17. 17.
    S. Osher and J. Sethian. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79: 12–49, 1988.MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Alex. P. Pentland. Perceptual organization and the representation of natural form. Artificial Intelligence, 28: 293–331, 1986.MathSciNetCrossRefGoogle Scholar
  19. 19.
    R. Ramamoorthi and J. Arvo. Creating generative models from range images. In SIGGRAPH ‘89 Proceedings, pages 195–204, August 1999.Google Scholar
  20. 20.
    A. Requicha and H. Voelcker. Boolean operations in solid modeling: Boundary evaluation and merging algorithms. Proceedings of the IEEE, 73 (1): 30–44, 1985.CrossRefGoogle Scholar
  21. 21.
    J.A. Sethian. Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge, second edition, 1999.MATHGoogle Scholar
  22. 22.
    L. Staib, X. Zeng, R. Schultz, and J. Duncan. Shape constraints in deformable models. In I. Bankman, editor, Handbook of Medical Imaging, chapter 9, pages 147–157. Academic Press, 2000.CrossRefGoogle Scholar
  23. 23.
    G. Stetten and S.M. Pizer. Medial node models to identify and measure objects in real-time 3d echocardiography. IEEE Transactions on Medical Imaging, 18 (10): 1025–1034, 1999.CrossRefGoogle Scholar
  24. 24.
    Richard Szeliski, David Tonnesen, and Demetri Terzopoulos. Modeling surfaces of arbitrary topology with dynamic particles. In Pmc. Fourth Int. Conf on Comp. Vision (ICCV’93) pages 82–87, Berlin, Germany, May 1993. IEEE Computer Society Press.Google Scholar
  25. 25.
    R. van den Boomgaard and A. W. M. Smeulders. The morphological structure of images, the differential equations of morphological scale-space. IEEE Trans. on Pat. Anal. and Mach. Intel 16 (11): 1101–1113, 1994.CrossRefGoogle Scholar
  26. 26.
    Ross T. Whitaker. Volumetric deformable models: Active blobs. In Richard A. Robb, editor, Visualization In Biomedical Computing 1994, pages 122–134, Mayo Clinic, Rochester, Minnesota, 1994. SPIE.Google Scholar
  27. Ross T Whitaker. Algorithms for implicit deformable models. In Fifth Intern. Conf. on Comp. Vision. IEEE, IEEE Computer Society Press, 1995.Google Scholar
  28. 28.
    Ross T. Whitaker. A level-set approach to 3D reconstruction from range data. Int. Jrnl. of Comp. Vision, October(3): 203–231, 1998.CrossRefGoogle Scholar
  29. 29.
    Ross T. Whitaker and David T Chen. Embedded active surfaces for volume visualization. In SPIE Medical Imaging 1994, Newport Beach, California, 1994.Google Scholar
  30. 30.
    Z. Wood, M. Desbrun, P Schröder, and D. Breen. Semi-regular mesh extraction from volumes. In Proceedings of Visualization 2000, pages 275–282, 2000.Google Scholar
  31. 31.
    Zhenyu Wu, Hsiao-Wen Chung, and Felix W. Wehrli. A Bayesian approach to subvoxel tissue classification in NMR microscopic images of trabecular bone. Journal of Computer Assisted Tomography, 12 (1): 1–9, 1988.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 2001

Authors and Affiliations

  • Ross Whitaker
    • 1
  • David Breen
    • 2
  • Ken Museth
    • 2
  • Neha Soni
    • 2
  1. 1.School of ComputingUniversity of UtahSalt Lake CityUSA
  2. 2.Computer Science DepartmentCalifornia Institute of TechnologyPasadenaUSA

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