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Exploring the discrimination power of the time domain for segmentation and characterization of lesions in serial MR data

  • Guido Gerig
  • Daniel Welti
  • Charles Guttmann
  • Alan Colchester
  • Gábor Székely
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)

Abstract

This paper presents a new methodology for the automatic segmentation and characterization of object changes in time series of three-dimensional data sets. The purpose of the analysis is a detection and characterization of objects based on their dynamic changes. The technique was inspired by procedures developed for the analysis of functional MRI data sets. After precise registration of serial volume data sets to 4-D data, we applied a new time series analysis taking into account the characteristic time function of variable lesions. The images were preprocessed with a correction of image field inhomogeneities and a normalization of the brightness function over the whole time series. This leads to the hypothesis that static regions remain unchanged over time, whereas local changes in tissue characteristics cause typical functions in the voxel’s time series. A set of features are derived from the time series and their derivatives, expressing probabilities for membership to the sought structures. These multiple sources of uncertain evidence were combined to a single evidence value using Dempster Shafer’s theory. Individual processing of a series of 3-D data sets is therefore replaced by a fully 4-D processing. To explore the sensitivity of time information, active lesions are segmented solely based on time fluctuation, neglecting absolute intensity information.

The project is driven by the objective of improving the segmentation and characterization of white matter lesions in serial MR data of multiple sclerosis patients. Pharmaceutical research and patient follow-up requires efficient and robust methods with high degree of automation. Further, an enhanced set of morphometric parameters might give a better insight into the course of the disease and therefore leads to a better understanding of the disease mechanism and of drug effects.

The new method has been applied to two time series from different patient studies, covering time resolutions of 12 and 24 data sets over a period of roughly one year. The results demonstrate that time evolution is a highly sensitive feature to detect fluctuating structures.

Keywords

Multiple Sclerosis Bias Correction White Matter Lesion Multiple Sclerosis Lesion Active Lesion 
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.

References

  1. 1.
    P.A. Bandettini, A Jesmanowicz, E.C. Wong, and J.S. Hyde, Processing strategies for time-course data sets in functional MRI of the human brain, Magnetic Resonance in Medicine, 30:161–173, 199Google Scholar
  2. 2.
    D.L.G. Hill, A. Simmons, C. Studholme, D.J. Hawkes, and S.C.R. Williams, Removal of stumulus corelated motion from echo plana fmri studies, In Proc. Soc. Magn. Res. 3rd annual meeting, page 840, 1995Google Scholar
  3. 3.
    Charles R.G. Guttmann et al., The Evolution of Multiple Sclerosis Lesions on Serial MR, AJNR 16:1481–1491, Aug 1995PubMedGoogle Scholar
  4. 4.
    R. Kikinis, M.E. Shenton, G. Gerig, J. Martin, M. Anderson, D. Metcalf, Ch.R.G. Guttmann, R.W. McCarley, B. Lorensen, H. Cline, F.A. Jolesz, Routine Quantitative Analysis of Brain and Cerebrospinal Fluid Spaces with MR Imaging, JMRI (Journal of Magnetic Resonance Imaging), Vol. 2 No. 6, pp. 619–629, Nov/Dec 1992CrossRefGoogle Scholar
  5. 5.
    A.C. Evans, J.A. Frank, J. Antel, and D.H. Miller, The role of MRI in clinical trials of multiple sclerosis: Comparison of image processing techniques. Annals of Neurology, 1996. In press.Google Scholar
  6. 6.
    A. Zijdenbos, A. Evans, F. Riahi, J. Sled, H.-C. Chui, and V. Kollokian, Automatic quantification of multiple sclerosis lesion volume using stereotaxic space, In forth Int. Conf. on Visualization in Biomedical Computing (VBC), Hamburg, Germany, 1996, pp. 439–448Google Scholar
  7. 7.
    M. Kamber, R. Shinghal, D.L. Collins, G.S. Francis, and A.C. Evans, Modelbased 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images. IEEE Transactions in Medical Imaging, 14(3):442–453, Sept. 1995CrossRefGoogle Scholar
  8. 8.
    G. Gerig, J. Martin, R. Kikinis, O. Kübler, M. Shenton and F. A. Jolesz, Unsupervised tissue type segmentation of 3D dual-echo MR head data, image and vision computing, IPMI 1991 special issue, vol. 10 No. 6, pp. 349–360, July/August 1992Google Scholar
  9. 9.
    S. Warfield et al., Automatic Identification of Gray Matter Structures from MRI to Improve the Segmentation of White Matter Lesions, Journal of Image Guided Surgery, Vol. 1, No. 6, June 1996, pp. 326–338CrossRefGoogle Scholar
  10. 10.
    Johnston et al., 1996, Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI. IEEE TMI 15(2):154–169.Google Scholar
  11. 11.
    Labeling of 4D structures in registered 3-D segmentations (exact reference to be added)Google Scholar
  12. 12.
    J-P. Thirion, New feature points based on geometric invariants for 3d image registration, Int. Journal of Computer Vision, 18(2):121–137, May 1996CrossRefGoogle Scholar
  13. 13.
    F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, Multi-Modality Image Registration by Maximization of Mutual Information, IEEE Trans. on Medical Imaging, 16(2), pp. 187–198, April 1997CrossRefPubMedGoogle Scholar
  14. 14.
    Christian Brechbühler, Guido Gerig, and Gabor Székely, Compensation of spatial inhomogeneity in MRI based on a multi-valued image model and a parametric bias estimate, Visualization in Biomedical Computing Proc. VBC’96, Lecture Notes in Computer Science, No. 1131, Springer, pp. 141–146, Sept. 1996Google Scholar
  15. 15.
    M. Styner and G. Gerig, Evaluation of 2D/3D bias correction with 1+1ES-optimization, Technical Report Image Science Lab, ETH Zurich, TR-179, 1997Google Scholar
  16. 16.
    Glenn Shafer, A Mathematical Theory of Evidence, Princeton, NJ: Princeton University Press, 1976Google Scholar
  17. 17.
    J. Gordon, and E.H. Shortliffe, The Dempster-Shafer Theory of Evidence, in: B.G. buchanan and E.H. Shortliffe (Eds.), Rule-Based Expert Systems, pp. 272–292, Addison-Wesley, 1985Google Scholar
  18. 18.
    Robert J. Safranek, Susan Gottschlich, Avinash C. Kak, Evidence Accumulation Using Binary Frames of Discernment for Verification Vision, Actions on Robotics and Automation, Vol. 6, No. 4, August 1990Google Scholar
  19. 19.
    European project on Brain Morphometry (BIOMORPH, EU-BIOMED2 project no BMH4-CT96-0845, 1996–1998Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Guido Gerig
    • 1
  • Daniel Welti
    • 1
  • Charles Guttmann
    • 2
  • Alan Colchester
    • 3
  • Gábor Székely
    • 1
  1. 1.Communication Technology Laboratory ETH-ZentrumSwiss Federal Institute of TechnologyZurichSwitzerland
  2. 2.Brigham and Women’s HospitalHarvard Medical SchoolBoston
  3. 3.University of Kent at CanterburyKentEngland

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