Lung Tissue Analysis: From Local Visual Descriptors To Global Modeling
| Publicatietype: | Proefschrift |
| Citatie: | Dic2018 |
| Jaar: | 2018 |
| School: | University of Geneva |
| Adres: | Geneva, Switzerland |
| URL: | https://archive-ouverte.unige.... |
| Samenvatting: | 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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| Toegevoegd door: | [] |
| Totaalscore: | 0 |
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