Minimum-description-length-based approach to CT reconstruction using truncated projections from objects with unknown boundaries

Tetsuya Yuasa; Balasigamani Devaraj; Yuuki Watanabe; Tomoo Sato; Yoshiaki Sasaki; Atsunori Hoshino; Humio Inaba; Takao Akatsuka

doi:10.1117/12.279640

14 October 1997 Minimum-description-length-based approach to CT reconstruction using truncated projections from objects with unknown boundaries

Tetsuya Yuasa, Balasigamani Devaraj, Yuuki Watanabe, Tomoo Sato, Yoshiaki Sasaki, Atsunori Hoshino, Humio Inaba, Takao Akatsuka

Author Affiliations +

Proceedings Volume 3167, Statistical and Stochastic Methods in Image Processing II; (1997) https://doi.org/10.1117/12.279640
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

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.

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Tetsuya Yuasa, Balasigamani Devaraj, Yuuki Watanabe, Tomoo Sato, Yoshiaki Sasaki, Atsunori Hoshino, Humio Inaba, and Takao Akatsuka "Minimum-description-length-based approach to CT reconstruction using truncated projections from objects with unknown boundaries", Proc. SPIE 3167, Statistical and Stochastic Methods in Image Processing II, (14 October 1997); https://doi.org/10.1117/12.279640

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