A Fuzzy-C-Means clustering algorithm for a volumetric analysis of paranasal sinus and nasal cavity cancers

K. Passera; P. Potepan; E. Setti; D. Vergnaghi; A. Sarti; L. Mainardi; S. Cerutti

doi:10.1109/IEMBS.2006.260334

A Fuzzy-C-Means clustering algorithm for a volumetric analysis of paranasal sinus and nasal cavity cancers

K. Passera, P. Potepan, E. Setti, D. Vergnaghi, A. Sarti, L. Mainardi, S. Cerutti

Fondazione IRCCS Istituto Nazionale dei Tumori

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

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	https://doi.org/10.1109/IEMBS.2006.260334
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

ASJC Scopus subject areas

Bioengineering

Access to Document

10.1109/IEMBS.2006.260334

Fingerprint Dive into the research topics of 'A Fuzzy-C-Means clustering algorithm for a volumetric analysis of paranasal sinus and nasal cavity cancers'. Together they form a unique fingerprint.

Cite this

Passera, K., Potepan, P., Setti, E., Vergnaghi, D., Sarti, A., Mainardi, L., & Cerutti, S. (2006). A Fuzzy-C-Means clustering algorithm for a volumetric analysis of paranasal sinus and nasal cavity cancers. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (pp. 3078-3081). [4029304] https://doi.org/10.1109/IEMBS.2006.260334

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

Passera, K, Potepan, P, Setti, E, Vergnaghi, D, Sarti, A, Mainardi, L & Cerutti, S 2006, A Fuzzy-C-Means clustering algorithm for a volumetric analysis of paranasal sinus and nasal cavity cancers. in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings., 4029304, pp. 3078-3081, 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, United States, 8/30/06. https://doi.org/10.1109/IEMBS.2006.260334

@inproceedings{732ac8035f384315b30031e9ef29b2f6,

title = "A Fuzzy-C-Means clustering algorithm for a volumetric analysis of paranasal sinus and nasal cavity cancers",

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.",

author = "K. Passera and P. Potepan and E. Setti and D. Vergnaghi and A. Sarti and L. Mainardi and S. Cerutti",

year = "2006",

doi = "10.1109/IEMBS.2006.260334",

language = "English",

isbn = "1424400325",

pages = "3078--3081",

booktitle = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

note = "28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 ; Conference date: 30-08-2006 Through 03-09-2006",

}

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

T2 - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06

Y2 - 30 August 2006 through 3 September 2006

ER -