Tomographic reconstruction of images from noisy projections: a preliminary study

Authors:
A. P. Dalgleish;D. L. Dowe;I. D. Svalbe
Affiliations:
Clayton School of IT, Monash University, Australia;Clayton School of IT, Monash University, Australia;School of Physics, Monash University, Australia
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

Citing 3
Cited 1

Image Representation Via a Finite Radon Transform

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)

Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
A geometry driven reconstruction algorithm for the mojette transform

DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery

Information-Theoretic Image Reconstruction and Segmentation from Noisy Projections

AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Although Computed Tomography (CT) is amature discipline, the development of techniques that will further reduce radiation dose are still essential. This paper makes steps towards projection and reconstruction methods which aim to assist in the reduction of this dosage, by studying the way noise propagates from projection space to image space. Inference methods Maximum Likelihood Estimation (MLE), Akaike's Information Criterion (AIC) and MinimumMessage Length (MML) are used to obtain accurate models obtained from minimal data.