Denoising PET images using singular value thresholding and Stein's unbiased risk estimate

Ulas Bagci; Daniel J. Mollura

doi:10.1007/978-3-642-40760-4_15

Denoising PET images using singular value thresholding and Stein's unbiased risk estimate

Ulas Bagci^*, Daniel J. Mollura

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

26 Scopus citations

Abstract

Image denoising is an important pre-processing step for accurately quantifying functional morphology and measuring activities of the tissues using PET images. Unlike structural imaging modalities, PET images have two difficulties: (1) the Gaussian noise model does not necessarily fit into PET imaging because the exact nature of noise propagation in PET imaging is not well known, and (2) PET images are low resolution; therefore, it is challenging to denoise them while preserving structural information. To address these two difficulties, we introduce a novel methodology for denoising PET images. The proposed method uses the singular value thresholding concept and Stein's unbiased risk estimate to optimize a soft thresholding rule. Results, obtained from 40 MRI-PET images, demonstrate that the proposed algorithm is able to denoise PET images successfully, while still maintaining the quantitative information.

Original language	English (US)
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages	115-122
Number of pages	8
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-40760-4_15
State	Published - 2013
Externally published	Yes
Event	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan Duration: Sep 22 2013 → Sep 26 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	8151 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/Territory	Japan
City	Nagoya
Period	9/22/13 → 9/26/13

Keywords

Denoising
PET
Singular Value Thresholding
Stein Risk Estimate

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-40760-4_15

Cite this

Bagci, U., & Mollura, D. J. (2013). Denoising PET images using singular value thresholding and Stein's unbiased risk estimate. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 3 ed., pp. 115-122). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-40760-4_15

Bagci, Ulas ; Mollura, Daniel J. / Denoising PET images using singular value thresholding and Stein's unbiased risk estimate. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3. ed. 2013. pp. 115-122 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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title = "Denoising PET images using singular value thresholding and Stein's unbiased risk estimate",

abstract = "Image denoising is an important pre-processing step for accurately quantifying functional morphology and measuring activities of the tissues using PET images. Unlike structural imaging modalities, PET images have two difficulties: (1) the Gaussian noise model does not necessarily fit into PET imaging because the exact nature of noise propagation in PET imaging is not well known, and (2) PET images are low resolution; therefore, it is challenging to denoise them while preserving structural information. To address these two difficulties, we introduce a novel methodology for denoising PET images. The proposed method uses the singular value thresholding concept and Stein's unbiased risk estimate to optimize a soft thresholding rule. Results, obtained from 40 MRI-PET images, demonstrate that the proposed algorithm is able to denoise PET images successfully, while still maintaining the quantitative information.",

keywords = "Denoising, PET, Singular Value Thresholding, Stein Risk Estimate",

author = "Ulas Bagci and Mollura, {Daniel J.}",

note = "Funding Information: This research is supported by CIDI, the intramural research program of the National Institute of Allergy and Infectious Diseases (NIAID) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB).; 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 ; Conference date: 22-09-2013 Through 26-09-2013",

year = "2013",

doi = "10.1007/978-3-642-40760-4_15",

language = "English (US)",

isbn = "9783642407598",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Bagci, U & Mollura, DJ 2013, Denoising PET images using singular value thresholding and Stein's unbiased risk estimate. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8151 LNCS, pp. 115-122, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 9/22/13. https://doi.org/10.1007/978-3-642-40760-4_15

Denoising PET images using singular value thresholding and Stein's unbiased risk estimate. / Bagci, Ulas; Mollura, Daniel J.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3. ed. 2013. p. 115-122 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Denoising PET images using singular value thresholding and Stein's unbiased risk estimate

AU - Bagci, Ulas

AU - Mollura, Daniel J.

N1 - Funding Information: This research is supported by CIDI, the intramural research program of the National Institute of Allergy and Infectious Diseases (NIAID) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB).

PY - 2013

Y1 - 2013

N2 - Image denoising is an important pre-processing step for accurately quantifying functional morphology and measuring activities of the tissues using PET images. Unlike structural imaging modalities, PET images have two difficulties: (1) the Gaussian noise model does not necessarily fit into PET imaging because the exact nature of noise propagation in PET imaging is not well known, and (2) PET images are low resolution; therefore, it is challenging to denoise them while preserving structural information. To address these two difficulties, we introduce a novel methodology for denoising PET images. The proposed method uses the singular value thresholding concept and Stein's unbiased risk estimate to optimize a soft thresholding rule. Results, obtained from 40 MRI-PET images, demonstrate that the proposed algorithm is able to denoise PET images successfully, while still maintaining the quantitative information.

AB - Image denoising is an important pre-processing step for accurately quantifying functional morphology and measuring activities of the tissues using PET images. Unlike structural imaging modalities, PET images have two difficulties: (1) the Gaussian noise model does not necessarily fit into PET imaging because the exact nature of noise propagation in PET imaging is not well known, and (2) PET images are low resolution; therefore, it is challenging to denoise them while preserving structural information. To address these two difficulties, we introduce a novel methodology for denoising PET images. The proposed method uses the singular value thresholding concept and Stein's unbiased risk estimate to optimize a soft thresholding rule. Results, obtained from 40 MRI-PET images, demonstrate that the proposed algorithm is able to denoise PET images successfully, while still maintaining the quantitative information.

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KW - Singular Value Thresholding

KW - Stein Risk Estimate

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ER -

Bagci U, Mollura DJ. Denoising PET images using singular value thresholding and Stein's unbiased risk estimate. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3 ed. 2013. p. 115-122. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-40760-4_15

Denoising PET images using singular value thresholding and Stein's unbiased risk estimate

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this