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Lung tissue analysis: from local visual descriptors to global modeling

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Defense Thèse de doctorat : Univ. Genève, 2018 - Sc. 5248 - 2018/08/27
Abstract 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.
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URN: urn:nbn:ch:unige-1113942
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DICENTE CID, Yashin. Lung tissue analysis: from local visual descriptors to global modeling. Université de Genève. Thèse, 2018. https://archive-ouverte.unige.ch/unige:111394

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Deposited on : 2018-11-26

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