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

doi:10.1002/mp.12531

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 journal › Article › Academic › peer-review

7 Citations (Web of Science)

88 Downloads (Pure)

Original language	English
Pages (from-to)	5835-5848
Number of pages	14
Journal	Medical Physics
Volume	44
Issue number	11
DOIs	https://doi.org/10.1002/mp.12531
Publication status	Published - 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

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10.1002/mp.12531

Full TextFinal published version, 1.33 MBLicence: Taverne

Cite this

@article{9b6c5ad986c74a19b1bf012271ce5fe0,

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 = nov,

doi = "10.1002/mp.12531",

language = "English",

volume = "44",

pages = "5835--5848",

journal = "Medical Physics",

issn = "0094-2405",

publisher = "Wiley",

number = "11",

}

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 journal › Article › Academic › peer-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

C2 - 28837224

VL - 44

SP - 5835

EP - 5848

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

IS - 11

ER -