A Fuzzy-C-Means Cluster ing Algorithm for a Volumetr ic Analysis of Paranasal Sinus and Nasal Cavity Cancer s

Author

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

Author_Institution

Dipt. di Ingegneria Biomed., Politecnico di Milano, Milan

fYear

2006

fDate

Aug. 30 2006-Sept. 3 2006

Firstpage

3078

Lastpage

3081

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

Keywords

biomedical MRI; cancer; fuzzy set theory; image segmentation; medical image processing; pattern clustering; tumours; fuzzy-C-means clustering algorithm; magnetic resonance data set; nasal cavity cancer; paranasal sinus; semiautomatic segmentation algorithm; volumetric analysis; Algorithm design and analysis; Cancer; Clustering algorithms; Image segmentation; Lesions; Magnetic analysis; Magnetic resonance; Neoplasms; Performance evaluation; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE

Conference_Location

New York, NY

ISSN

1557-170X

Print_ISBN

1-4244-0032-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2006.260334

Filename

4462447