Abstract
In this paper, a semi-automatic segmentation algorithm for volumetric analysis of paranasal sinus and nasal cavity cancers is presented and validated. The algorithm, based on a semi-supervised Fuzzy-C-means method, was applied 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 64 tumor-contained slices. Method performances are tested by both a numerical and a clinical validation. Results show that the proposed method has a higher accuracy in quantifying lesion area than a Region Growing algorithm and it can be applied in the evaluation of tumor response to therapy.
| Original language | English |
|---|---|
| Title of host publication | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Pages | 3078-3081 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2006 |
| Event | 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States Duration: Aug 30 2006 → Sep 3 2006 |
Other
| Other | 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 |
|---|---|
| Country | United States |
| City | New York, NY |
| Period | 8/30/06 → 9/3/06 |
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ASJC Scopus subject areas
- Bioengineering
Cite this
A Fuzzy-C-Means clustering algorithm for a volumetric analysis of paranasal sinus and nasal cavity cancers. / Passera, K.; Potepan, P.; Setti, E.; Vergnaghi, D.; Sarti, A.; Mainardi, L.; Cerutti, S.
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. 2006. p. 3078-3081 4029304.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A Fuzzy-C-Means clustering algorithm for a volumetric analysis of paranasal sinus and nasal cavity cancers
AU - Passera, K.
AU - Potepan, P.
AU - Setti, E.
AU - Vergnaghi, D.
AU - Sarti, A.
AU - Mainardi, L.
AU - Cerutti, S.
PY - 2006
Y1 - 2006
N2 - In this paper, a semi-automatic segmentation algorithm for volumetric analysis of paranasal sinus and nasal cavity cancers is presented and validated. The algorithm, based on a semi-supervised Fuzzy-C-means method, was applied 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 64 tumor-contained slices. Method performances are tested by both a numerical and a clinical validation. Results show that the proposed method has a higher accuracy in quantifying lesion area than a Region Growing algorithm and it can be applied in the evaluation of tumor response to therapy.
AB - In this paper, a semi-automatic segmentation algorithm for volumetric analysis of paranasal sinus and nasal cavity cancers is presented and validated. The algorithm, based on a semi-supervised Fuzzy-C-means method, was applied 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 64 tumor-contained slices. Method performances are tested by both a numerical and a clinical validation. Results show that the proposed method has a higher accuracy in quantifying lesion area than a Region Growing algorithm and it can be applied in the evaluation of tumor response to therapy.
UR - http://www.scopus.com/inward/record.url?scp=34047159580&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34047159580&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2006.260334
DO - 10.1109/IEMBS.2006.260334
M3 - Conference contribution
AN - SCOPUS:34047159580
SN - 1424400325
SN - 9781424400324
SP - 3078
EP - 3081
BT - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ER -