Clustering Algorithms for MRI

  • Vito Di Gesù
  • Robert De La Paz
  • Wiliams A. Hanson
  • Ralph Bernstein
Part of the Lecture Notes in Medical Informatics book series (LNMED, volume 45)


Magnetic Resonance Imaging (MRI) plays a relevant role in the design of systems for computer assisted diagnosis. MR-images are multi-dimensional in nature; physicians have to combine several perceptual information images to perform the tissue classification needed for diagnosis. Automatic clustering methods help to discriminate relevant features and to perform a preliminary segmentation of the image; it can guide the final manual classification of body-tissues. Three clustering techniques and their integration in a MRI-system are described. Their performance and accuracy was evaluated on synthetic and real image-data. A comparison of our approach with the tissue-classification done by a radiologist was performed.


MRI clustering classification data-analysis 


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  1. [1]
    M. Purcell, H.C. Torrey and R.V. Pound, “Resonance absorption by nuclear magnetic moments in a solid”, Phys.Rev., N. 69, pp. 37–38, 1946.CrossRefGoogle Scholar
  2. [2]
    F. Bloch, “Nuclear Induction”, Phys.Rev., N. 70, pp. 460–473, 1946.CrossRefGoogle Scholar
  3. [3]
    P.C. Lauterbur, “Image Formation by Induced Local Interactions: Examples Employing Nuclear Magnetic Resonance”, Nature, N. 242, pp. 190–191, 1973.CrossRefGoogle Scholar
  4. [4]
    E. Herskovits, M. Walker, “Computer-Aided Classification of Magnetic-Resonance Images”, Tech.Rep. N.KSL-89-47, Medical Computer Science, Stanford University, 1989.Google Scholar
  5. [5]
    R. Dann, J. Hoford, et al., “Preliminary Clinical Evaluation of Multi-Resolution Elastic Matching Software”, Tech.Report, MS-CIS-85-35, Department of Computer Science and Information Science, University of Pennsylvania, 1988.Google Scholar
  6. [6]
    W.A. Hanson, E. Herskovits, R. De La Paz and R. Bernstein, “A Maximum-Likelihood Classifier for Automated Radiologic Diagnosis”, 1988.Google Scholar
  7. [7]
    V. Di Gesu’, R.L. De La Paz, W.A. Hanson, R. Bernstein, “A comparison of Clustering Algorithms for MRI”, PASC-Tech.Rep., N., 1989.Google Scholar
  8. [8]
    V. Di Gesu’, et all., “Hierarchical Clustering Algorithms: a comparative analysis”, in preparation.Google Scholar
  9. [9]
    R.O. Duda and P.E. Hart, “Pattern Classification and Scene Analysis”, John Willey and Sons, 1973.zbMATHGoogle Scholar
  10. [10]
    J.C.Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms”, Plenum Press, NY, 1987.Google Scholar
  11. [11]
    V. Di Gesù, “A Clustering Approach to Texture Classification”, in Real Time Object and Environment Measurement and Classification, A.K.Anil Jain ed., NATO ASI Series F, Vol.42, Springer Verlag, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Vito Di Gesù
    • 1
  • Robert De La Paz
    • 2
  • Wiliams A. Hanson
    • 3
  • Ralph Bernstein
    • 3
  1. 1.Dipartimento di Matematica e ApplicazioniUniversity of PalermoItaly
  2. 2.Department of RadiologyStanford UniversityUSA
  3. 3.I.B.M. PASCPalo AltoUSA

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