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.
authors.affiliation: Dipt. di Ingegneria Biomed., Politecnico di Milano, Milan, Italy. katia.passera@polimi.it
authors.forename: K
authors.initials: K
authors.lastname: Passera
authors.forename: P
authors.initials: P
authors.lastname: Potepan
authors.forename: E
authors.initials: E
authors.lastname: Setti
authors.forename: D
authors.initials: D
authors.lastname: Vergnaghi
authors.forename: A
authors.initials: A
authors.lastname: Sarti
authors.forename: L
authors.initials: L
authors.lastname: Mainardi
authors.forename: S
authors.initials: S
authors.lastname: Cerutti
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D009296:Nasal Cavity / Q000473:pathology*; D009669:Nose Neoplasms / Q000473:pathology*; D010255:Paranasal Sinus Neoplasms / Q000473:pathology*