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Journal of Combinatorial Optimization

Erschienen in:

10.04.2021

Regularized graph cuts based discrete tomography reconstruction methods

verfasst von: Marina Marčeta, Tibor Lukić

Erschienen in: Journal of Combinatorial Optimization | Ausgabe 4/2022

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Abstract

The topic of this paper includes graph cuts based computed tomography reconstruction methods in binary and multi-gray level cases. This approach combines the graph cuts and a gradient based method. The present paper introduces and analyses the shape circularity as a new regularization and incorporates it in a graph cuts based computed tomography reconstruction approach, thus introducing a new energy-minimization based reconstruction algorithm for binary tomography. Proposed method is capable for reconstructions in cases of limited projection view availability. Results of experimental evaluation of the considered graph cuts type reconstruction methods for both binary and multi-level tomography are presented.

Vorheriger Artikel Intersections and circuits in sets of line segments

Nächster Artikel Relative-perfectness of discrete gradient vector fields and multi-parameter persistent homology

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Titel: Regularized graph cuts based discrete tomography reconstruction methods
verfasst von: Marina Marčeta
Tibor Lukić
Publikationsdatum: 10.04.2021
Verlag: Springer US
Erschienen in: Journal of Combinatorial Optimization / Ausgabe 4/2022
Print ISSN: 1382-6905
Elektronische ISSN: 1573-2886
DOI: https://doi.org/10.1007/s10878-021-00730-4

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