ID | 116149 |
Title Alternative | 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
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Author |
Yamaguchi, Yusaku
Shikoku Medical Center for Children and Adults
Abou Al-Ola, Omar M.
Tanta University
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Keywords | power-divergence measure
computed tomography
iterative reconstruction
maximum-likelihood expectation-maximization method
continuous-time image reconstruction
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Content Type |
Thesis or Dissertation
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Description | 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.
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Journal Title |
Entropy
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ISSN | 10994300
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Publisher | MDPI
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Volume | 23
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Issue | 8
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Start Page | 1005
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Published Date | 2021-07-31
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Remark | 内容要旨・審査要旨・論文本文の公開
本論文は,著者Ryosuke Kasaiの学位論文として提出され,学位審査・授与の対象となっている。 |
Rights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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EDB ID | |
DOI (Published Version) | |
URL ( Publisher's Version ) | |
FullText File | |
language |
eng
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TextVersion |
ETD
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MEXT report number | 甲第3606号
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Diploma Number | 甲保第54号
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Granted Date | 2022-03-23
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Degree Name |
Doctor of Health Science
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Grantor |
Tokushima University
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departments |
Medical Sciences
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