ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm

Katia M. Passera; Paolo Potepan; Luca Brambilla; Luca T. Mainardi

ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm

Katia M. Passera, Paolo Potepan, Luca Brambilla, Luca T. Mainardi

Fondazione IRCCS Istituto Nazionale dei Tumori

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 Tl-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.

Original language	English
Title of host publication	Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
Pages	1218-1221
Number of pages	4
Publication status	Published - 2008
Event	30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada Duration: Aug 20 2008 → Aug 25 2008

Other

Other	30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
Country/Territory	Canada
City	Vancouver, BC
Period	8/20/08 → 8/25/08

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Signal Processing
Biomedical Engineering
Health Informatics

Cite this

ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm. / Passera, Katia M.; Potepan, Paolo; Brambilla, Luca et al.

Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". 2008. p. 1218-1221 4649382.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Passera, KM, Potepan, P, Brambilla, L & Mainardi, LT 2008, ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm. in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"., 4649382, pp. 1218-1221, 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08, Vancouver, BC, Canada, 8/20/08.

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abstract = "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 Tl-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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N2 - 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 Tl-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.

AB - 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 Tl-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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ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm

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ASJC Scopus subject areas

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