Basgier, Dennis (2013)
Visual Analysis of Local Correspondence in Segmentation Quality.
Technische Universität Darmstadt
Bachelorarbeit, Bibliographie
Kurzbeschreibung (Abstract)
The Bachelor-Thesis presents a new interactive system for visual and exploratory analysis of local correspondence in segmentation quality. Segmentations of several samples of one organ are analyzed on the basis of pairwise distances between a reference- and a test mesh, which is extracted from the organ segmentation. The tool features several views on the data (Coloring, threshold based highlighting, average mesh visualization) and a set of analysis methods (clustering, cluster quality evaluation, dimension reduction) to extract new information, such as reoccurring regions or patterns of low quality segmentation. Segmentation algorithm developers can use the visual information for gaining knowledge on how their algorithms work. This insight can be beneficial to the improvement of the algorithms. The software is optimized for analyzing medical image segmentation, but can also be translated to countless domains, as it simply operates on the extracted mesh data.
| Typ des Eintrags: | Bachelorarbeit |
|---|---|
| Erschienen: | 2013 |
| Autor(en): | Basgier, Dennis |
| Art des Eintrags: | Bibliographie |
| Titel: | Visual Analysis of Local Correspondence in Segmentation Quality |
| Sprache: | Englisch |
| Publikationsjahr: | 2013 |
| Kurzbeschreibung (Abstract): | The Bachelor-Thesis presents a new interactive system for visual and exploratory analysis of local correspondence in segmentation quality. Segmentations of several samples of one organ are analyzed on the basis of pairwise distances between a reference- and a test mesh, which is extracted from the organ segmentation. The tool features several views on the data (Coloring, threshold based highlighting, average mesh visualization) and a set of analysis methods (clustering, cluster quality evaluation, dimension reduction) to extract new information, such as reoccurring regions or patterns of low quality segmentation. Segmentation algorithm developers can use the visual information for gaining knowledge on how their algorithms work. This insight can be beneficial to the improvement of the algorithms. The software is optimized for analyzing medical image segmentation, but can also be translated to countless domains, as it simply operates on the extracted mesh data. |
| Freie Schlagworte: | Forschungsgruppe Medical Computing (MECO), Forschungsgruppe Visual Search and Analysis (VISA), Image segmentation, Evaluation of segmentation, Clustering, Medical applications, Cluster analysis, Visual analysis |
| Zusätzliche Informationen: | 84 p. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 12 Nov 2018 11:16 |
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