Vascular registration in photoacoustic imaging by low-rank alignment via foreground,background and complement decomposition

Ryoma Bise, Yingqiang Zheng, Imari Sato, Masakazu Toi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Photoacoustic (PA) imaging has been gaining attention as a new imaging modality that can non-invasively visualize blood vessels inside biological tissues. In the process of imaging large body parts through multi-scan fusion,alignment turns out to be an important issue,since body motion degrades image quality. In this paper,we carefully examine the characteristics of PA images and propose a novel registration method that achieves better alignment while effectively decomposing the shot volumes into low-rank foreground (blood vessels),dense background (noise),and sparse complement (corruption) components on the basis of the PA characteristics. The results of experiments using a challenging real data-set demonstrate the efficacy of the proposed method,which significantly improved image quality,and had the best alignment accuracy among the state-of-the-art methods tested.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsLeo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin
PublisherSpringer Verlag
Pages326-334
Number of pages9
ISBN (Print)9783319467252
DOIs
Publication statusPublished - Jan 1 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9902 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Photoacoustic Imaging
Photoacoustic effect
Registration
Alignment
Complement
Blood Vessels
Blood vessels
Decomposition
Imaging techniques
Decompose
Image Quality
Image quality
Imaging
Biological Tissue
Modality
Efficacy
Fusion
Fusion reactions
Tissue
Motion

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Bise, R., Zheng, Y., Sato, I., & Toi, M. (2016). Vascular registration in photoacoustic imaging by low-rank alignment via foreground,background and complement decomposition. In L. Joskowicz, M. R. Sabuncu, W. Wells, G. Unal, & S. Ourselin (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 326-334). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9902 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46726-9_38

Vascular registration in photoacoustic imaging by low-rank alignment via foreground,background and complement decomposition. / Bise, Ryoma; Zheng, Yingqiang; Sato, Imari; Toi, Masakazu.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. ed. / Leo Joskowicz; Mert R. Sabuncu; William Wells; Gozde Unal; Sebastian Ourselin. Springer Verlag, 2016. p. 326-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9902 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Bise, R, Zheng, Y, Sato, I & Toi, M 2016, Vascular registration in photoacoustic imaging by low-rank alignment via foreground,background and complement decomposition. in L Joskowicz, MR Sabuncu, W Wells, G Unal & S Ourselin (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9902 LNCS, Springer Verlag, pp. 326-334. https://doi.org/10.1007/978-3-319-46726-9_38
Bise R, Zheng Y, Sato I, Toi M. Vascular registration in photoacoustic imaging by low-rank alignment via foreground,background and complement decomposition. In Joskowicz L, Sabuncu MR, Wells W, Unal G, Ourselin S, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag. 2016. p. 326-334. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-46726-9_38
Bise, Ryoma ; Zheng, Yingqiang ; Sato, Imari ; Toi, Masakazu. / Vascular registration in photoacoustic imaging by low-rank alignment via foreground,background and complement decomposition. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. editor / Leo Joskowicz ; Mert R. Sabuncu ; William Wells ; Gozde Unal ; Sebastian Ourselin. Springer Verlag, 2016. pp. 326-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{67216e8637654d7489204d37702dd515,
title = "Vascular registration in photoacoustic imaging by low-rank alignment via foreground,background and complement decomposition",
abstract = "Photoacoustic (PA) imaging has been gaining attention as a new imaging modality that can non-invasively visualize blood vessels inside biological tissues. In the process of imaging large body parts through multi-scan fusion,alignment turns out to be an important issue,since body motion degrades image quality. In this paper,we carefully examine the characteristics of PA images and propose a novel registration method that achieves better alignment while effectively decomposing the shot volumes into low-rank foreground (blood vessels),dense background (noise),and sparse complement (corruption) components on the basis of the PA characteristics. The results of experiments using a challenging real data-set demonstrate the efficacy of the proposed method,which significantly improved image quality,and had the best alignment accuracy among the state-of-the-art methods tested.",
author = "Ryoma Bise and Yingqiang Zheng and Imari Sato and Masakazu Toi",
year = "2016",
month = "1",
day = "1",
doi = "10.1007/978-3-319-46726-9_38",
language = "English",
isbn = "9783319467252",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "326--334",
editor = "Leo Joskowicz and Sabuncu, {Mert R.} and William Wells and Gozde Unal and Sebastian Ourselin",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",
address = "Germany",

}

TY - GEN

T1 - Vascular registration in photoacoustic imaging by low-rank alignment via foreground,background and complement decomposition

AU - Bise, Ryoma

AU - Zheng, Yingqiang

AU - Sato, Imari

AU - Toi, Masakazu

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Photoacoustic (PA) imaging has been gaining attention as a new imaging modality that can non-invasively visualize blood vessels inside biological tissues. In the process of imaging large body parts through multi-scan fusion,alignment turns out to be an important issue,since body motion degrades image quality. In this paper,we carefully examine the characteristics of PA images and propose a novel registration method that achieves better alignment while effectively decomposing the shot volumes into low-rank foreground (blood vessels),dense background (noise),and sparse complement (corruption) components on the basis of the PA characteristics. The results of experiments using a challenging real data-set demonstrate the efficacy of the proposed method,which significantly improved image quality,and had the best alignment accuracy among the state-of-the-art methods tested.

AB - Photoacoustic (PA) imaging has been gaining attention as a new imaging modality that can non-invasively visualize blood vessels inside biological tissues. In the process of imaging large body parts through multi-scan fusion,alignment turns out to be an important issue,since body motion degrades image quality. In this paper,we carefully examine the characteristics of PA images and propose a novel registration method that achieves better alignment while effectively decomposing the shot volumes into low-rank foreground (blood vessels),dense background (noise),and sparse complement (corruption) components on the basis of the PA characteristics. The results of experiments using a challenging real data-set demonstrate the efficacy of the proposed method,which significantly improved image quality,and had the best alignment accuracy among the state-of-the-art methods tested.

UR - http://www.scopus.com/inward/record.url?scp=84996486800&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84996486800&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46726-9_38

DO - 10.1007/978-3-319-46726-9_38

M3 - Conference contribution

AN - SCOPUS:84996486800

SN - 9783319467252

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 326

EP - 334

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings

A2 - Joskowicz, Leo

A2 - Sabuncu, Mert R.

A2 - Wells, William

A2 - Unal, Gozde

A2 - Ourselin, Sebastian

PB - Springer Verlag

ER -