AI 2009: Advances in Artificial Intelligence

Volume 5866 of the series Lecture Notes in Computer Science pp 170-179

Information-Theoretic Image Reconstruction and Segmentation from Noisy Projections

  • Gerhard VisserAffiliated withMonash University
  • , David L. DoweAffiliated withMonash University
  • , Imants D. SvalbeAffiliated withMonash University

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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.