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.
A Fuzzy-C-Means Cluster ing Algorithm for a Volumetr ic Analysis of Paranasal Sinus and Nasal Cavity Cancers. Publishing Authors By Initials
A Fuzzy-C-Means Cluster ing Algorithm for a Volumetr ic Analysis of Paranasal Sinus and Nasal Cavity Cancers. Journal Published:
PUBLICATION TYPE: Journal Article
Journal: Conference proceedings : ... Annual International
VOLUME: 1
Page Numbers: 3078-81
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ISSN: 1557-170X
DAY: 23
MONTH: 10
YEAR: 2006
A Fuzzy-C-Means Cluster ing Algorithm for a Volumetr ic Analysis of Paranasal Sinus and Nasal Cavity Cancers. Information
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LANGUAGE: eng
NlmUniqueID: 101243413
A Fuzzy-C-Means Cluster ing Algorithm for a Volumetr ic Analysis of Paranasal Sinus and Nasal Cavity Cancers. Keywords Mesh Terms:
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Grant and Affiliation Information for A Fuzzy-C-Means Cluster ing Algorithm for a Volumetr ic Analysis of Paranasal Sinus and Nasal Cavity Cancers.
AFFILIATION: Dipt. di Ingegneria Biomed., Politecnico di Milano, Milan.
Country: United States
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MEDLINETA: Conf Proc IEEE Eng Med Biol So
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