Automated Interpretation of Cardiac Scintigrams

  • Jens Richter
  • Anders Ericsson
  • Kalle Åström
  • Fredrik Kahl
  • Lars Edenbrant
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


The purpose of this study was to develop an automated method for the segmentation of the heart in a 3-D cardiac scintigram. This is immediately useful for eliminating a manual step in a previous version of a decision support system.

The automatic segmentation method uses a statistical 3D-model, inspired by Active Shape, which locates the base and apex automatically from a cardiac scintigram. Key features of this algorithm are that it can handle cases where there is no or very little activity in the apex and also if there are additional parts of the heart where there is little activity. The algorithm has been tested on approximately 2000 cardiac scintigrams.


  1. 1.
    A. Baumberg and Hogg D. Learning flexible models from image sequences. In Proc. European Conf. on Computer Vision, ECCV’94, pages 299–308, 1994.Google Scholar
  2. 2.
    A. Benayoun, Ayache N., and Cohen I. Adaptive meshes and nonrigid motion computation. In Proc. International Conference on Pattern Recognition, ICPR’94, 1994.Google Scholar
  3. 3.
    T.F. Cootes, A. Hill, C.J Taylor, and Haslam J. Use of active shape models for locating structure in medical images. IEEE Trans. medical imaging, 12(6):355–365, 1994.Google Scholar
  4. 4.
    T.F Cootes and C.J. Taylor. Statistical Models of Appearance for Computer Vision. University of Manchester, 2001.Google Scholar
  5. 5.
    Rhodri H. Davies, Tim F. Cootes, John C. Waterton, and Chris J. Taylor. An efficient method for constructing optimal statistical shape models. In Medical Image Computing and Computer-Assisted Intervention MICCAI’2001, pages 57–65, 2001.Google Scholar
  6. 6.
    Rhodri H. Davies, Carole J. Twining, Tim F. Cootes, John C. Waterton, and Chris J. Taylor. A minimum description length approach to statistical shape modeling. IEEE Trans. medical imaging, 21(5):525–537, 2002.CrossRefGoogle Scholar
  7. 7.
    C. Kambhamettu and D.B. Goldgof. Points correspondences recovery in non-rigid motion. In Proc. Conf. Computer Vision and Pattern Recognition, CVPR’ 92, pages 222–237, 1992.Google Scholar
  8. 8.
    A. Kelemen, G. Szekely, and Gerig G. Elastic model-based segmentation of 3d neuroradiological data sets. IEEE Trans. medical imaging, 18(10):828–839, 1999.CrossRefGoogle Scholar
  9. 9.
    Y. Wang, B.S. Peterson, and L.H Staib. Shape-based 3d surface correspondence using geodesics and local geometry. In Proc. Conf. Computer Vision and Pattern Recognition, CVPR’00, pages 644–651, 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jens Richter
    • 2
  • Anders Ericsson
    • 1
  • Kalle Åström
    • 1
  • Fredrik Kahl
    • 1
  • Lars Edenbrant
    • 3
  1. 1.Mathematics, Centre for Mathematical Sciences, Lund Institute of TechnologyLund UniversitySweden
  2. 2.WeAidUIDEONLundSweden
  3. 3.Department of Clinical PhysiologyLund UniversityLundSweden

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