GMM Based Simultaneous Reconstruction and Segmentation in X-Ray CT Application

Shi Yan; Yiqiu Dong

doi:10.1007/978-3-030-75549-2_40

GMM Based Simultaneous Reconstruction and Segmentation in X-Ray CT Application

Shi Yan^*, Yiqiu Dong

^*Corresponding author for this work

Nankai University

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

In this paper, we propose a new simultaneous reconstruction and segmentation (SRS) model in X-ray computed tomography (CT). The new SRS model is based on the Gaussian mixture model (GMM). In order to transform non-separable log-sum term in GMM into a form that can be easy solved, we introduce an auxiliary variable, which in fact plays a segmentation role. The new SRS model is much simpler comparing with the models derived from the hidden Markov measure field model (HMMFM). Numerical results show that the proposed model achieves improved results than other methods, and the CPU time is greatly reduced.

Original language	English
Title of host publication	Scale Space and Variational Methods in Computer Vision
Editors	Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon
Publisher	Springer
Publication date	2021
Pages	503-515
ISBN (Print)	9783030755485
DOIs	https://doi.org/10.1007/978-3-030-75549-2_40
Publication status	Published - 2021
Event	8^th International Conference on Scale Space and Variational Methods in Computer Vision - Virtual, Online Duration: 16 May 2021 → 20 May 2021 https://ssvm2021.sciencesconf.org/

Conference

Conference	8^th International Conference on Scale Space and Variational Methods in Computer Vision
City	Virtual, Online
Period	16/05/2021 → 20/05/2021
Internet address	https://ssvm2021.sciencesconf.org/

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12679 LNCS
ISSN	0302-9743

Bibliographical note

Funding Information:
The work was supported by Villum Investigator grant 25893 from the Villum Foundation.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Alternating minimization method
Fast algorithm
Gaussian mixture model
Inverse problem
Simultaneous reconstruction and segmentation
X-ray CT

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Yan, Shi ; Dong, Yiqiu. / GMM Based Simultaneous Reconstruction and Segmentation in X-Ray CT Application. Scale Space and Variational Methods in Computer Vision. editor / Abderrahim Elmoataz ; Jalal Fadili ; Yvain Quéau ; Julien Rabin ; Loïc Simon. Springer, 2021. pp. 503-515 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12679 LNCS).

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abstract = "In this paper, we propose a new simultaneous reconstruction and segmentation (SRS) model in X-ray computed tomography (CT). The new SRS model is based on the Gaussian mixture model (GMM). In order to transform non-separable log-sum term in GMM into a form that can be easy solved, we introduce an auxiliary variable, which in fact plays a segmentation role. The new SRS model is much simpler comparing with the models derived from the hidden Markov measure field model (HMMFM). Numerical results show that the proposed model achieves improved results than other methods, and the CPU time is greatly reduced.",

keywords = "Alternating minimization method, Fast algorithm, Gaussian mixture model, Inverse problem, Simultaneous reconstruction and segmentation, X-ray CT",

author = "Shi Yan and Yiqiu Dong",

note = "Funding Information: The work was supported by Villum Investigator grant 25893 from the Villum Foundation. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 8<sup>th</sup> International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021 ; Conference date: 16-05-2021 Through 20-05-2021",

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Yan, S & Dong, Y 2021, GMM Based Simultaneous Reconstruction and Segmentation in X-Ray CT Application. in A Elmoataz, J Fadili, Y Quéau, J Rabin & L Simon (eds), Scale Space and Variational Methods in Computer Vision. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12679 LNCS, pp. 503-515, 8^th International Conference on Scale Space and Variational Methods in Computer Vision, Virtual, Online, 16/05/2021. https://doi.org/10.1007/978-3-030-75549-2_40

GMM Based Simultaneous Reconstruction and Segmentation in X-Ray CT Application. / Yan, Shi; Dong, Yiqiu.
Scale Space and Variational Methods in Computer Vision. ed. / Abderrahim Elmoataz; Jalal Fadili; Yvain Quéau; Julien Rabin; Loïc Simon. Springer, 2021. p. 503-515 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12679 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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AU - Yan, Shi

AU - Dong, Yiqiu

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N2 - In this paper, we propose a new simultaneous reconstruction and segmentation (SRS) model in X-ray computed tomography (CT). The new SRS model is based on the Gaussian mixture model (GMM). In order to transform non-separable log-sum term in GMM into a form that can be easy solved, we introduce an auxiliary variable, which in fact plays a segmentation role. The new SRS model is much simpler comparing with the models derived from the hidden Markov measure field model (HMMFM). Numerical results show that the proposed model achieves improved results than other methods, and the CPU time is greatly reduced.

AB - In this paper, we propose a new simultaneous reconstruction and segmentation (SRS) model in X-ray computed tomography (CT). The new SRS model is based on the Gaussian mixture model (GMM). In order to transform non-separable log-sum term in GMM into a form that can be easy solved, we introduce an auxiliary variable, which in fact plays a segmentation role. The new SRS model is much simpler comparing with the models derived from the hidden Markov measure field model (HMMFM). Numerical results show that the proposed model achieves improved results than other methods, and the CPU time is greatly reduced.

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KW - Fast algorithm

KW - Gaussian mixture model

KW - Inverse problem

KW - Simultaneous reconstruction and segmentation

KW - X-ray CT

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DO - 10.1007/978-3-030-75549-2_40

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A2 - Elmoataz, Abderrahim

A2 - Fadili, Jalal

A2 - Quéau, Yvain

A2 - Rabin, Julien

A2 - Simon, Loïc

PB - Springer

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Yan S, Dong Y. GMM Based Simultaneous Reconstruction and Segmentation in X-Ray CT Application. In Elmoataz A, Fadili J, Quéau Y, Rabin J, Simon L, editors, Scale Space and Variational Methods in Computer Vision. Springer. 2021. p. 503-515. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12679 LNCS). doi: 10.1007/978-3-030-75549-2_40

GMM Based Simultaneous Reconstruction and Segmentation in X-Ray CT Application

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