In 2018, ImageCLEF proposed a task using CT (Computed Tomography) scans of patients with tuberculosis (TB). The task was divided into three subtasks: multi{drug resistance detection, TB type classi _cation, and severity scoring. In this work we present a graph model of the lungs capable of characterizing TB patients with di_erent lung problems. The graph contains a _xed number of nodes with weighted edges based on dissimilarity measures between texture descriptors computed in the nodes. This model encodes the texture distribution along the lungs, making it suitable for describing patients with di_erent TB types. The results show the strength of the technique, leading to high results in the three subtasks.