@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",
}