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 | Canada |
| City | Vancouver, BC |
| Period | 8/20/08 → 8/25/08 |
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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; 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 proceeding › Conference contribution
}
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.
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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 -