In this paper we present a novel technique for characterizing and classifying 3D textured volumes belonging to different lung tissues in 3D CT images.We build a volume based 3D descriptor, robust to changes of size, rigid spatial transformations and texture variability, thanks to the integration of Riesz-wavelet features within a Covariance-based descriptor formulation. 3D Riesz features characterize the morphology of tissue density thanks to their response to changes in intensity in CT images. These features are encoded in a Covariancebased descriptor formulation: this provides a compact and flexible representation thanks to the use of feature variations rather than dense features themselves, and adds robustness to spatial changes. Furthermore, the particular symmetric definite positive matrix form of these descriptors causes them to lay in a Riemannian manifold. Thus, descriptors can be compared with analytical measures, and accurate techniques from Machine Learning and clustering can be adapted to their spatial domain. Additionally we present a classification model following a “Bag of Covariance Descriptors” paradigm in order to distinguish three different nodule tissue types in CT: solid, ground-glass opacity (GGO), and healthy. Classification accuracy is estimated based on an acquired dataset of 95 patients with manually delineated ground truth by radiology specialists in 3D. The promising outcomes of the presented method support a future aim for automated lung nodule detection and computerized diagnosis assistance applications.