Lung tissue analysis: from local visual descriptors to global modeling
Yashin Dicente Cid · Archive ouverte UNIGE (University of Geneva) · 2018
Medical imaging plays an important role in patient diagnosis and treatment planning. A standard procedure to assess a respiratory disease is a CT scan of the chest, where radiologists can detect subtle alterations in the lung tissue. This thesis aims at describing the lung tissue in CT scans, both from a local and a global perspective. It explores all the steps involved in the pipeline for the automatic analysis of the lung tissue: the initial lung segmentation, the division of lung fields into subregions, the extraction of local biomedical features, and the assembly of local features to form a global model. A new tissue descriptor is presented, as well as a novel graph-based model that provides a global characterization of the lung tissue. In addition, this thesis describes a new on-line platform where clinicians can extract state-of-the-art computerized image-based features.