Title :
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
fDate :
Aug. 30 2006-Sept. 3 2006
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;
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2006.260334
