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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
Pages1218-1221
Number of pages4
Publication statusPublished - 2008
Event30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada
Duration: Aug 20 2008Aug 25 2008

Other

Other30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
CountryCanada
CityVancouver, BC
Period8/20/088/25/08

Fingerprint

Adenocarcinoma
Tumors
Magnetic resonance
Neoplasms
Magnetic Resonance Spectroscopy
Therapeutics

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

Passera, K. M., Potepan, P., Brambilla, L., & Mainardi, L. T. (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" (pp. 1218-1221). [4649382]

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

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 proceedingConference 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.
Passera KM, Potepan P, Brambilla L, Mainardi LT. 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". 2008. p. 1218-1221. 4649382
Passera, Katia M. ; Potepan, Paolo ; Brambilla, Luca ; Mainardi, Luca T. / ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". 2008. pp. 1218-1221
@inproceedings{d8929989fcd646d5bf1b4090f450ff89,
title = "ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm",
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.",
author = "Passera, {Katia M.} and Paolo Potepan and Luca Brambilla and Mainardi, {Luca T.}",
year = "2008",
language = "English",
isbn = "9781424418152",
pages = "1218--1221",
booktitle = "Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - {"}Personalized Healthcare through Technology{"}",

}

TY - GEN

T1 - ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm

AU - Passera, Katia M.

AU - Potepan, Paolo

AU - Brambilla, Luca

AU - Mainardi, Luca T.

PY - 2008

Y1 - 2008

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.

UR - http://www.scopus.com/inward/record.url?scp=61849138210&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=61849138210&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781424418152

SP - 1218

EP - 1221

BT - Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"

ER -