Diagnosis of solitary lung nodules using the local form of Ripley's K function applied to three-dimensional CT data.
This paper analyzes the application of Ripley's K function to characterize lung nodules as malignant or benign in computerized tomography images. The proposed characterization method is based on a selection of measures from Ripley's K function to discriminate between benign and malignant nodules, using stepwise discriminant analysis. Based on the selected measures, a linear discriminant analysis procedure is performed once again in order to predict the classification of each nodule. To evaluate the ability of these features to discriminate the nodules, a set of tests was carried out using a sample of 39 pulmonary nodules, 29 benign and 10 malignant. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator's performance. The best setting of the analyzed function in the tested sample presented 70.0% of sensitivity but with 100.0% of specificity and 92.3% of accuracy. Thus, preliminary results of this approach are very promising regarding its contribution to the diagnosis of pulmonary nodules, but it still needs to be tested with larger series and associated to other quantitative imaging methods in order to improve global performance.
da Silva
Erick Corrêa
EC
Federal University of Maranhão-UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil. erick@labpai.dee.ufma.br
Silva
Aristófanes Corrêa
AC
de Paiva
Anselmo Cardoso
AC
Nunes
Rodolfo Acatauassú
RA
Gattass
Marcelo
M
D016002:Discriminant Analysis; D006801:Humans; D016014:Linear Models; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*