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Proceedings Paper

Minimum-description-length-based approach to CT reconstruction using truncated projections from objects with unknown boundaries
Author(s): Tetsuya Yuasa; Balasigamani Devaraj; Yuuki Watanabe; Tomoo Sato; Yoshiaki Sasaki; Atsunori Hoshino; Humio Inaba; Takao Akatsuka
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Paper Abstract

This paper considers the interior problem of CT reconstruction in which outer data are deficient in each projection. It is effective to this problem to restrict the parameters, i.e., the pixels, to be estimated to the region in which an object exists. We investigate this problem using the minimum description length principle proposed by Rissanen which is the amount of information required to describe a model based on information theory. Reconstruction algorithm and the data structure for this model to reduce amounts of calculation and memory are proposed. Finally, its effectiveness is shown by simulation.

Paper Details

Date Published: 14 October 1997
PDF: 10 pages
Proc. SPIE 3167, Statistical and Stochastic Methods in Image Processing II, (14 October 1997);
Show Author Affiliations
Tetsuya Yuasa, Yamagata Univ. (Japan)
Balasigamani Devaraj, Biophotonics Information Labs. (Japan)
Yuuki Watanabe, Biophotonics Information Labs. (Japan)
Tomoo Sato, Biophotonics Information Labs. (Japan)
Yoshiaki Sasaki, Biophotonics Information Labs. (Japan)
Atsunori Hoshino, Yamagata Univ. (Japan)
Humio Inaba, Tohoku Institute of Technology (Japan)
Takao Akatsuka, Yamagata Univ. (Japan)

Published in SPIE Proceedings Vol. 3167:
Statistical and Stochastic Methods in Image Processing II
Francoise J. Preteux; Jennifer L. Davidson; Edward R. Dougherty, Editor(s)

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