| ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm. | |
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MedLine Citation:
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PMID: 19162885 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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In this paper, a semi-automatic segmentation method for volume assessment of Intestinal-type adenocarcinoma (ITAC) is presented and validated. The method is based on a Gaussian hidden Markov random field (GHMRF) model that represents an advanced version of a finite Gaussian mixture (FGM) model as it encodes spatial information through the mutual influences of neighboring sites. To fit the GHMRF model an expectation maximization (EM) algorithm is used. We applied the method to a magnetic resonance data sets (each of them composed by T1-weighted, Contrast Enhanced T1-weighted and T2-weighted images) for a total of 49 tumor-contained slices. We tested GHMRF performances with respect to FGM by both a numerical and a clinical evaluation. Results show that the proposed method has a higher accuracy in quantifying lesion area than FGM and it can be applied in the evaluation of tumor response to therapy. |
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Authors:
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Katia M Passera; Paolo Potepan; Luca Brambilla; Luca T Mainardi |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Volume: 2008 ISSN: 1557-170X ISO Abbreviation: Conf Proc IEEE Eng Med Biol Soc Publication Date: 2008 |
Date Detail:
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Created Date: 2009-02-16 Completed Date: 2009-05-05 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101243413 Medline TA: Conf Proc IEEE Eng Med Biol Soc Country: United States |
Other Details:
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Languages: eng Pagination: 1218-21 Citation Subset: IM |
Affiliation:
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Dipartimento di Ingegneria Biomedica, Politecnico di Milano, Italy. katia.passera@polimi.it |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Adenocarcinoma
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pathology* Algorithms* Humans Magnetic Resonance Imaging Markov Chains Models, Statistical* Paranasal Sinus Neoplasms / pathology* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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