In 2019, ImageCLEF proposed a task using CT (Computed Tomography) scans of patients with tuberculosis (TB). The task was divided into two subtasks: TB severity assessment (SVR subtask) and automatic CT report generation (CTR subtask). In this work we present our participation in the task. We participated with a graph model of the lungs with a morphology{based structure that was previously validated for the detection of TB types. The graph is based on a parcellation of the lung elds into supervoxels, where each region is identied as a node of the graph. A weighted edge is dened between nodes representing adjacent regions. The associated weight is computed as the distance between 3D texture descriptors extracted from the two connected regions. This model encodes the texture distribution along the lungs, making it suitable for detecting the tissue abnormalities present in TB patients. In this work we explore two techniques to classify these graphs: (i) a lung descriptor vector based on the aggregation of graph centrality measures and (ii) a set of 2D histograms encoding the binary distribution of node features. The nal classication is performed with support vector machines for the lung descriptor vector and with convolutional neural networks for the 2D histograms. The results show the strength of the technique, leading to 6th and 3rd place in the SVR and CTR subtasks, respectively.