In this work we present our participation in the ImageCLEF 2017 tuberculosis task. The task consists of detecting five tuberculosis (TB) types and predicting drug resistance from lung CT (Computed Tomography) volumes. Our approach is based on a previously developed non-parametric method. Tested on CT images of Chronic Obstructive Pulmonary Disease (COPD) patients, it consists of describing each subject as a collection of local feature descriptors embedded in a dissimilarity space. The set of local features was extended for this work adding new 3D texture descriptors. The results shows that our approach is able to characterize several TB types, achieving a Cohen’s Kappa coefficient of 0.1533, but does not suit for predicting drug resistance were it only achieved an AUC of 0.5241.