A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation

Houda Hanzouli-Ben Salah, Jerome Lapuyade-Lahorgue, Julien Bert, Didier Benoit, Philippe Lambin, Angela Van Baardwijk, Emmanuel Monfrini, Wojciech Pieczynski, Dimitris Visvikis, Mathieu Hatt*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Original languageEnglish
Pages (from-to)5835-5848
Number of pages14
JournalMedical Physics
Volume44
Issue number11
DOIs
Publication statusPublished - Nov 2017

Keywords

  • Bayesian inference
  • computed tomography (CT)
  • hidden Markov trees (HMT)
  • positron emission tomography (PET)
  • segmentation
  • wavelet and contourlet analysis
  • CELL LUNG-CANCER
  • TUMOR DELINEATION
  • F-18-FDG PET
  • CT IMAGES
  • TRACER UPTAKE
  • MODEL
  • RECONSTRUCTION
  • BRAIN
  • CLASSIFICATION
  • QUANTITATION

Cite this

Hanzouli-Ben Salah, H., Lapuyade-Lahorgue, J., Bert, J., Benoit, D., Lambin, P., Van Baardwijk, A., ... Hatt, M. (2017). A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation. Medical Physics, 44(11), 5835-5848. https://doi.org/10.1002/mp.12531
Hanzouli-Ben Salah, Houda ; Lapuyade-Lahorgue, Jerome ; Bert, Julien ; Benoit, Didier ; Lambin, Philippe ; Van Baardwijk, Angela ; Monfrini, Emmanuel ; Pieczynski, Wojciech ; Visvikis, Dimitris ; Hatt, Mathieu. / A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation. In: Medical Physics. 2017 ; Vol. 44, No. 11. pp. 5835-5848.
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title = "A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation",
keywords = "Bayesian inference, computed tomography (CT), hidden Markov trees (HMT), positron emission tomography (PET), segmentation, wavelet and contourlet analysis, CELL LUNG-CANCER, TUMOR DELINEATION, F-18-FDG PET, CT IMAGES, TRACER UPTAKE, MODEL, RECONSTRUCTION, BRAIN, CLASSIFICATION, QUANTITATION",
author = "{Hanzouli-Ben Salah}, Houda and Jerome Lapuyade-Lahorgue and Julien Bert and Didier Benoit and Philippe Lambin and {Van Baardwijk}, Angela and Emmanuel Monfrini and Wojciech Pieczynski and Dimitris Visvikis and Mathieu Hatt",
year = "2017",
month = "11",
doi = "10.1002/mp.12531",
language = "English",
volume = "44",
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journal = "Medical Physics",
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Hanzouli-Ben Salah, H, Lapuyade-Lahorgue, J, Bert, J, Benoit, D, Lambin, P, Van Baardwijk, A, Monfrini, E, Pieczynski, W, Visvikis, D & Hatt, M 2017, 'A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation', Medical Physics, vol. 44, no. 11, pp. 5835-5848. https://doi.org/10.1002/mp.12531

A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation. / Hanzouli-Ben Salah, Houda; Lapuyade-Lahorgue, Jerome; Bert, Julien; Benoit, Didier; Lambin, Philippe; Van Baardwijk, Angela; Monfrini, Emmanuel; Pieczynski, Wojciech; Visvikis, Dimitris; Hatt, Mathieu.

In: Medical Physics, Vol. 44, No. 11, 11.2017, p. 5835-5848.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation

AU - Hanzouli-Ben Salah, Houda

AU - Lapuyade-Lahorgue, Jerome

AU - Bert, Julien

AU - Benoit, Didier

AU - Lambin, Philippe

AU - Van Baardwijk, Angela

AU - Monfrini, Emmanuel

AU - Pieczynski, Wojciech

AU - Visvikis, Dimitris

AU - Hatt, Mathieu

PY - 2017/11

Y1 - 2017/11

KW - Bayesian inference

KW - computed tomography (CT)

KW - hidden Markov trees (HMT)

KW - positron emission tomography (PET)

KW - segmentation

KW - wavelet and contourlet analysis

KW - CELL LUNG-CANCER

KW - TUMOR DELINEATION

KW - F-18-FDG PET

KW - CT IMAGES

KW - TRACER UPTAKE

KW - MODEL

KW - RECONSTRUCTION

KW - BRAIN

KW - CLASSIFICATION

KW - QUANTITATION

U2 - 10.1002/mp.12531

DO - 10.1002/mp.12531

M3 - Article

VL - 44

SP - 5835

EP - 5848

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

IS - 11

ER -

Hanzouli-Ben Salah H, Lapuyade-Lahorgue J, Bert J, Benoit D, Lambin P, Van Baardwijk A et al. A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation. Medical Physics. 2017 Nov;44(11):5835-5848. https://doi.org/10.1002/mp.12531