Date of Award

Fall 2011

Degree Type

Thesis

Degree Name

Master of Computer Science (MCS)

Department

Computing and Software

Supervisor

Ned. Nedialkov

Language

English

Abstract

A computerized tomography scan enables the visualization of an object interior without opening it up. This technique is used in many fields e.g. in medical imaging, geology, and industry. To obtain information about an object, exterior measurements by means of X-rays are performed. Then, to reconstruct an image of the object’s interior, image-reconstructions methods are applied. The problem of reconstructing images from measurements of X-ray radiation belongs to the class of inverse problems. A class of important methods for inverse problems is Algebraic Reconstruction Techniques (ART). The performance of these methods depends on the choice of a relaxation parameter.

In this thesis, we compare numerically various ART methods, namely Kaczmarz, symmetric Kaczmarz, randomized Kaczmarz and simultaneous ART. We perform an extensive numerical investigation of the behaviour of these methods, and in particular, study how they perform with respect to this relaxation parameter. We propose a simple heuristic for finding a good relaxation parameter for each of these methods. Comparisons of the new proposed strategy with a previously proposed one shows that our strategy has a slightly better performance in terms of relative error, relative residual and image discrepancy of the reconstructed image. Both strategies showed relatively close numerical results, but interestingly enough, for different values of this parameter.

McMaster University Library

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