Information-Theoretic Image Reconstruction and Segmentation from Noisy Projections

  • Gerhard Visser
  • David L. Dowe
  • Imants D. Svalbe
Conference paper

DOI: 10.1007/978-3-642-10439-8_18

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5866)
Cite this paper as:
Visser G., Dowe D.L., Svalbe I.D. (2009) Information-Theoretic Image Reconstruction and Segmentation from Noisy Projections. In: Nicholson A., Li X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science, vol 5866. Springer, Berlin, Heidelberg

Abstract

The minimum message length (MML) principle for inductive inference has been successfully applied to image segmentation where the images are modelled by Markov random fields (MRF). We have extended this work to be capable of simultaneously reconstructing and segmenting images that have been observed only through noisy projections. The noise added to each projection depends on the classes of the pixels (material) that it passes through. The intended application is in low-dose (low-flux) X-ray computed tomography (CT) where irregular projections are used.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gerhard Visser
    • 1
  • David L. Dowe
    • 1
  • Imants D. Svalbe
    • 1
  1. 1.Monash UniversityMelbourneAustralia

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