In 2017, the ImageCLEF benchmark proposed a task based on CT (Computed Tomography) images of patients with tuberculosis (TB). This task was divided into two subtasks: multi-drug resistance prediction, and TB type detection. In this work we present a graph-model of the lungs capable of characterizing TB patients with different lung problems. This graph contains a fixed number of nodes with weighted edges based on distance measures between texture descriptors computed on the nodes. This model attempts to encode the texture distribution along the lungs, making it suitable for describing patients with different tuberculosis types. The results show the strength of the technique, leading to best results in the competition for multi-drug resistance (AUC = 0.5825) and good results in the tuberculosis type detection (Cohen’s Kappa coef. = 0.1623), with many of the good runs being fairly close.