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A Minimally-Interactive Watershed Algorithm Designed for Efficient CTA Bone Removal

  • Horst K. Hahn
  • Markus T. Wenzel
  • Olaf Konrad-Verse
  • Heinz-Otto Peitgen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4241)

Abstract

We introduce a novel minimally-interactive watershed algorithm that needs no initial parameterization, but lets the user refine the automatic segmentation close to real-time. In contrast to previous proposals, our algorithm encapsulates all time consuming calculation in a processing step executed only once. Thereby, a hierarchical subdivision of the incoming image data is generated. This subdivision serves as a basis for computing automatic segmentation results according to a given multi-dimensional classification scheme as well as for interactive refinement according to local markers. We have successfully applied our algorithm to efficiently removing bone structures from computed tomography angiography data, which is among the very challenging segmentation problems in medical image analysis.

Keywords

Medical Image Analysis Bone Removal Direct Volume Rendering Bone Segmentation Watershed Transform 
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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References

  1. 1.
    Alyassin, A.M., Avinash, G.B.: Semiautomatic bone removal technique from CT angiography data. Med Imaging. In: Proc. SPIE 2005, vol. 4322, pp. 1273–1283 (2001)Google Scholar
  2. 2.
    Digabel, H., Lantuèjoul, C.: Iterative algorithms. In: Chermant, J.L. (ed.) Proc. 2nd European Symp. Quantitative Analysis of Micro-structures in Material Science, Biology and Medicine, pp. 85–99 (1978)Google Scholar
  3. 3.
    Fiebich, M., Straus, C.M., Sehgal, V., Renger, B.C., Doi, K., Hoffmann, K.R.: Automatic bone segmentation technique for CT angiographic studies. J. Comput. Assist. Tomogr. 23(1), 155–161 (1999)CrossRefGoogle Scholar
  4. 4.
    Grau, V., Mewes, A.U.J., Alcañiz, M., Kikinis, R., Warfield, S.K.: Improved watershed transform for medical image segmentation using prior information. IEEE Trans. Med. Imaging 23(4), 447–458 (2004)CrossRefGoogle Scholar
  5. 5.
    Hahn, H.K.: Morphological Volumetry Theory, Concepts, and Application to Quantitative Medical Imaging. Ph.D. thesis, University of Bremen (2005)Google Scholar
  6. 6.
    Hahn, H.K., Peitgen, H.-O.: The skull stripping problem in MRI solved by a single 3D watershed transform. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 134–143. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Hahn, H.K., Peitgen, H.-O.: IWT—Interactive Watershed Transform: A hierarchical method for efficient interactive and automated segmentation of multidimensional grayscale images. In: Med. Imaging, Proc. SPIE, vol. 5032, pp. 643–653 (2003)Google Scholar
  8. 8.
    Kang, Y., Engelke, K., Kalender, W.A.: A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data. IEEE Trans. Med. Imaging 22(5), 586–598 (2003)CrossRefGoogle Scholar
  9. 9.
    Moore, E.A., Grieve, J.P., Jäger, H.R.: Robust processing of intracranial CT angiograms for 3D volume rendering. Eur. J. Radiol. 11(1), 137–141 (2001)CrossRefGoogle Scholar
  10. 10.
    Mullick, R., Avila, R., Knoplioch, J., Mallya, Y., Platt, J., Senzig, R.: Automatic bone removal for abdomen CTA: A clinical review. In: Proc. RSNA (2002)Google Scholar
  11. 11.
    Raman, R., Raman, B., Hundt, W., Stucker, D., Napel, S., Rubin, G.D.: Improved speed of bone removal in CT angiography (CTA) using automated targeted morphological separation: Method and evaluation in CTA of lower extremity occlusive disease (LEOD). Radiology 225(P), 647 (2002)Google Scholar
  12. 12.
    Roerdink, J.B.T.M., Meijster, A.: The watershed transform: Definitions, algorithms, and parallelization strategies. Fundamenta Informaticae 41, 187–228 (2000)MATHMathSciNetGoogle Scholar
  13. 13.
    Suryanaranayanan, S., Mullick, R., Mallya, Y., Wood, C., McCullough, C., Thielen, K.: Automatic bone removal for head CTA: A preliminary review. In: Proc. RSNA (2003)Google Scholar
  14. 14.
    van Straten, M., Venema, H.W., Streekstra, G.J., den Heeten, G.J., Majoie, C.B.L.M.: Removal of bone in CT angiography of the cervical arteries by piecewise matched mask bone elimination. Medical Physics 31 (10), 2924–2933 (2004)CrossRefGoogle Scholar
  15. 15.
    Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans. Pattern Analysis Machine Intel 13(6), 583–598 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Horst K. Hahn
    • 1
  • Markus T. Wenzel
    • 1
  • Olaf Konrad-Verse
    • 1
  • Heinz-Otto Peitgen
    • 1
  1. 1.MeVis, Center for Medical Diagnostic Systems and VisualizationBremenGermany

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