Skip to main content
SpringerLink
Log in

Application of Bayesian Methods to Segmentation in Medical Images

  • Conference paper
Stochastic Models, Statistical Methods, and Algorithms in Image Analysis

Part of the book series: Lecture Notes in Statistics ((LNS,volume 74))

Abstract

Two applications of Bayesian image analysis in medicine are discussed, simultaneous segmentation of 3D X-ray Computerized Tomography (CT) scenes and detection of microcalcifications in mammograms. Segmentation by iterative optimization based on Bayesian decision theory appears to be very fruitful if suitable models can be designed to capture prior knowledge of the structure of the image. In practice this requires development of spatial models in which the use of local context is extended from nearest neighbour relations to more complex descriptions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Coggins J M (1988) Computer aided object definition from digital images. Proc. Joint US Scand. symp. on future directions of computer aided radiotherapy, San Antonio, Texas.

    Google Scholar 

  2. Hansen F R, Elliot H (1982) Image segmentation using simple Markov random fields. Comput. Graph. Image. Process 20: 101–132

    Google Scholar 

  3. Besag J E (1986) On the statistical analysis of dirty pictures. J. Royal. Statist. Soc., Ser. B 48: 259–302

    MathSciNet  MATH  Google Scholar 

  4. Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern. Anal. Machine. Intel!. 6: 721–741

    Google Scholar 

  5. Dubes R C, Jain A K (1989) Random field models in image analysis Journal of Applied Statistics 16 (2): 131–164

    Article  Google Scholar 

  6. Marroquin J, Mitter S, Poggio T (1987) Probabilistic solution of ill-posed problems in computational vision. Journal of the American Statistical Association 82(397):76–89

    Google Scholar 

  7. Kirkpatrick S, Gelatt C D, Vecchi M P (1983) Optimization by simulated annealing. Science 220: 671–680

    Article  MathSciNet  Google Scholar 

  8. Schlegel W (1987) Progress and trends in computer assisted radiation therapy. In: Proc. Computer Assisted Radiology ‘87, Springer-Verlag, Berlin

    Google Scholar 

  9. -D Imaging in Medicine (1990) Höhne K H, Fuchs H and Pizer S M (Ed.). NATO ASI Series F Vol. 60, Springer-Verlag, Berlin.

    Google Scholar 

  10. Grenander U and Keenan D M (1989) Towards Automated Image Analysis, Journal of Applied Statistics 16 (2): 207–221

    Article  Google Scholar 

  11. Amit Y, Grenander U and Piccioni M (1989) Structural image restoration through deformable templates. (preprint) Division of Applied Mathematics, Brown University.

    Google Scholar 

  12. Karssemeijer N (1989) A statistical method for automatic labeling of tissues in medical images. Machine Vision and Applications 3: 75–86

    Article  Google Scholar 

  13. Fam B H, Olson S L, Winter P F and Scholz F J (1988) Algorithm for the detection of fine clustered calcifications on film mammograms Radiology 169 (2): 333–337

    Google Scholar 

  14. Chan H P, Doi K, Vyborny C J, Lam K L and Schmidt R A (1988) Computer aided detection of microcalcifications in mammograms. Investigative Radiology 23 (9): 664–671

    Google Scholar 

  15. Davies D H, Dance D R and Jones C H (1990) Automatic detection of clusters of calcifications in digital mammograms. SPIE Vol. 1233 Medical Imaging IV, Image Processing: 185–189

    Google Scholar 

  16. Karssemeijer N and van Erning L J Th 0 (1991) Iso-precision scaling of digitized mammograms to facilitate image analysis. SPIE Vol. 1445 Medical Imaging V, Image Processing: 166–177

    Chapter  Google Scholar 

  17. Ballard D H (1984) Parameter nets. Artificial Intelligence 22: 235–267

    Article  Google Scholar 

  18. Illingworth J and Kittler J (1988) A survey of the Hough Transform. Comput. Graph. Image. Process 44: 87–116

    Article  Google Scholar 

  19. Ballard D H (1986) Cortical connections and parallel processing: Structure and function. The Behavioral and Brain Sciences, 9: 67–120

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, University of Nijmegen, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands

    N. Karssemeijer

Authors
  1. N. Karssemeijer
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Istituto per le Applicazioni del Calcolo, C.N.R., Viale del Policlinico 137, 00161, Rome, Italy

    Piero Barone  & Arnoldo Frigessi  & 

  2. Dipartimento di Matematica Pura ed Applicata, Università di L’Aquila, L’Aquila, Italy

    Mauro Piccioni

Rights and permissions

Reprints and permissions

Copyright information

© 1992 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Karssemeijer, N. (1992). Application of Bayesian Methods to Segmentation in Medical Images. In: Barone, P., Frigessi, A., Piccioni, M. (eds) Stochastic Models, Statistical Methods, and Algorithms in Image Analysis. Lecture Notes in Statistics, vol 74. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2920-9_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-2920-9_14

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-97810-9

  • Online ISBN: 978-1-4612-2920-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation