A Fuzzy-C-means clustering algorithm for a volumetric analysis of paranasal sinus and nasal cavity cancers.
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.
Passera
K
K
Dipt. di Ingegneria Biomed., Politecnico di Milano, Milan, Italy. katia.passera@polimi.it
Potepan
P
P
Setti
E
E
Vergnaghi
D
D
Sarti
A
A
Mainardi
L
L
Cerutti
S
S
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*