Information-Theoretic Image Reconstruction and Segmentation from Noisy Projections

Authors:
Gerhard Visser;David L. Dowe;Imants D. Svalbe
Affiliations:
Monash University, Melbourne, Australia 3800;Monash University, Melbourne, Australia 3800;Monash University, Melbourne, Australia 3800
Venue:
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Year:
2009

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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.