Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures

Persistent ID (NDL): info:ndljp/pid/12263929

Material type: 博士論文

Author: Kasai, Ryosukeほか

Publisher: MDPI

Publication date: 2021-07-31

Material Format: Digital

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Name of awarding university/degree: 徳島大学,博士（保健学）

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Note (General)：: The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the di...

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Material Type: 博士論文
ISSN: 1099-4300
Title: Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures
Volume: 23 8
Author/Editor: Kasai, Ryosuke
Yamaguchi, Yusaku
Kojima, Takeshi
Abou Al-Ola, Omar M.
Yoshinaga, Tetsuya
Author Heading: Kasai, Ryosuke
Yamaguchi, Yusaku
Kojima, Takeshi
Abou Al-Ola, Omar M.
Yoshinaga, Tetsuya
Publication, Distribution, etc.: MDPI
Publication Date: 2021-07-31
Publication Date (W3CDTF): 2021-07-31
Alternative Title: 拡張指数型測度族の最小化に基づき導出された雑音に頑健な画像再構成
Periodical title: Entropy
Degree grantor/type: 徳島大学
Date Granted: 2022-03-23
Date Granted (W3CDTF): 2022-03-23
Dissertation Number: 甲第3606号
Degree Type: 博士（保健学）
Conferring No. (Dissertation): 甲第3606号
Text Language Code: eng
Subject Heading: power-divergence measure
computed tomography
iterative reconstruction
maximum-likelihood expectation-maximization method
continuous-time image reconstruction
Target Audience: 一般
Note (General): The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the difference between the measured and forward projections. Iterative image reconstruction algorithms provide significant improvements over transform methods in computed tomography. In this paper, we present an extended class of power-divergence measures (PDMs), which includes a large set of distance and relative entropy measures, and propose an iterative reconstruction algorithm based on the extended PDM (EPDM) as an objective function for the optimization strategy. For this purpose, we introduce a system of nonlinear differential equations whose Lyapunov function is equivalent to the EPDM. Then, we derive an iterative formula by multiplicative discretization of the continuous-time system. Since the parameterized EPDM family includes the Kullback–Leibler divergence, the resulting iterative algorithm is a natural extension of the maximum-likelihood expectation-maximization (MLEM) method. We conducted image reconstruction experiments using noisy projection data and found that the proposed algorithm outperformed MLEM and could reconstruct high-quality images that were robust to measured noise by properly selecting parameters.
DOI: info:doi/10.3390/e23081005
https://doi.org/info:doi/10.3390/e23081005
Persistent ID (NDL): info:ndljp/pid/12263929
https://dl.ndl.go.jp/pid/12263929
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Collection (Materials For Handicapped People:1): テキストデータ
Collection (particular): 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文
https://dl.ndl.go.jp/collections/A00014
Acquisition Basis: 博士論文（自動収集）
Date Accepted (W3CDTF): 2022-05-09T11:57:37+09:00
Format (IMT): application/pdf
Access Restrictions: 国立国会図書館内限定公開
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Availability of remote photoduplication service: 可
Periodical Title (URI): http://repo.lib.tokushima-u.ac.jp/116149
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