The present work seeks to develop a computational tool to suggest about the malignancy or benignity of Solitary Lung Nodules by the analysis of texture and geometry measures obtained from computadorized tomography images.

Four groups of methods are proposed with the purpose of suggesting the diagnosis for such nodule. The groups of methods are divided according to their common characteristics. Group I includes methods based on texture adapted for 3D, such as the histogram, the Spatial Gray Level Dependence Method, the Gray Level Difference Method and Gray Level Run Length Matrices. Group II also deals with the texture of nodules, but uses four statistical functions denominated semivariogram, semimadogram, covariogram and correlogram. Group III describes measures based only on the geometry of the nodule, such as convexity, sphericity, and measures based on the curvature. Finally, Group IV analyzes the Gini coefficient and nodule skeleton methods, which take into account both the nodule's geometry and its texture.

A sample with 36 nodules, 29 benign and 7 malignant, was analyzed and the preliminary results of this approach are very promising in characterizing lung nodules. Most groups of proposed methods have the area under the ROC curve value above $0.800$, using Fisher's Linear Discriminant Analysis and Multilayer Perceptron Neural Networks. This means that the proposed methods have great potential in thePress any key to return to index.n of Solitary Lung Nodules.