Reduced projection angles for binary tomography with particle aggregation

Abstract

This paper extends particle aggregate reconstruction technique (PART), a reconstruction algorithm for binary tomography based on the movement of particles. PART supposes that pixel values are particles, and that particles diffuse through the image, staying together in regions of uniform pixel value known as aggregates. In this work, a variation of this algorithm is proposed and a focus is placed on reducing the number of projections and whether this impacts the reconstruction of images. The algorithm is tested on three phantoms of varying sizes and numbers of forward projections and compared to filtered back projection, a random search algorithm and to SART, a standard algebraic reconstruction method. It is shown that the proposed algorithm outperforms the aforementioned algorithms on small numbers of projections. This potentially makes the algorithm attractive in scenarios where collecting less projection data are inevitable.

Contributors

Mohammad Majid al-Rifaie:

ORCID: http://orcid.org/0000-0002-1798-9615

[e-mail]

Tim Blackwell: [e-mail]

Journal

Journal ID (nlm-ta): Evol Intell

Journal ID (iso-abbrev): Evol Intell

Title: Evolutionary Intelligence

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 1864-5909

ISSN (Electronic): 1864-5917

Publication date (Electronic): 8 August 2016

Publication date PMC-release: 8 August 2016

Publication date (Print): 2016

Volume: 9

Issue: 3

Pages: 67-79

Affiliations

Department of Computing, Goldsmiths, University of London, London, SE14 6NW UK

Article

Publisher ID: 140

DOI: 10.1007/s12065-016-0140-7

PMC ID: 5002048

Copyright statement: © The Author(s) 2016

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Categories

Subject: Special Issue

Custom metadata

issue-copyright-statement © Springer-Verlag Berlin Heidelberg 2016