Diagnosis of lung nodule using semivariogram and geometric measures in computerized tomography images.
This paper uses the geostatistical function - semivariogram and a set of 3D geometric measures - sphericity index, convexity index, extrinsic and intrinsic curvature index and surface type, to characterize lung nodules as malignant or benign in computerized tomography images. Based on a sample of 31 nodules, 25 benign and 6 malignant, these methods are first analyzed individually and then jointly, with techniques for classification and analysis (stepwise discriminant analysis, leave-one-out and ROC curve). We have concluded that the individual measures and their combinations produce good results in the diagnosis of lung nodules.
Silva
Aristófanes C
AC
Federal University of Maranhão (UFMA), Department of Electrical Engineering, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580 São Luís, MA, Brazil. ari@dee.ufma.br
Carvalho
Paulo Cezar P
PC
Gattass
Marcelo
M
D016002:Discriminant Analysis; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D012372:ROC Curve; D014057:Tomography, X-Ray Computed / Q000379:methods*