Pattern Analysis & Applications
Computer Methods and Programs in Biomedicine
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Pulmonary nodule classification aided by clustering
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Lung structure classification using 3D geometric measurements and SVM
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
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The large incidence of lung cancer in Brazil and around the world, in addition to its difficult diagnosis, especially in the initial stages, has been driving efforts to develop tools that support image-based diagnosis. The main objective is to avoid invasive procedures, which usually pose risks to patients. This work uses Getis spatial autocorrelation statistics, Getis^*, plus its accumulated forms to verify patterns occurring in geographic areas, aiming to indicate the nature of the lung nodule (benign or malignant). Nodule analysis is performed on its volume in a directional way, checking whether there are distances inside the nodule with large intensity variability of the voxels, for malignant and benign nodules. The classification is done by selecting the best four features from the 2400 generated features, for each of the Getis estimates. The Lung Image Database Consortium (LIDC) is used to verify the efficacy of the measures in the diagnosis. Results have shown that all of the Getis estimates succeeded in the discrimination of nodules in LIDC, with accuracy higher than 80% and confirmed by three different classifiers.