Bartschat, Andreas ; Toso, Lorenzo ; Stegmaier, Johannes ; Kuijper, Arjan ; Mikut, Ralf ; Köhler, Bernd ; Allgeier, Stephan (2016)
Automatic Corneal Tissue Classification Using Bag-Of-Visual-Words Approaches.
Forum Bildverarbeitung 2016. Karlsruhe (01.-02. Dezember 2016)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Corneal confocal microscopy is a promising diagnostic method for peripheral neuropathy. It allows the recording of the sub-basal nerve plexus (SNP) and enables the morphological analysis of peripheral nerves. This work evaluates classification models for real-time evaluation of cornea images in order to find suitable methods for an automatic focus adaptation to the SNP. The analyzed Bag-of-Visual-Words method leads to models with an accuracy of 0.9, even on a small training dataset, and a runtime of 18 ms per image. Furthermore, the analysis of the support vector machine real-valued output shows high potential for the implementation of real-time focus optimization methods.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Bartschat, Andreas ; Toso, Lorenzo ; Stegmaier, Johannes ; Kuijper, Arjan ; Mikut, Ralf ; Köhler, Bernd ; Allgeier, Stephan |
| Art des Eintrags: | Bibliographie |
| Titel: | Automatic Corneal Tissue Classification Using Bag-Of-Visual-Words Approaches |
| Sprache: | Englisch |
| Publikationsjahr: | Dezember 2016 |
| Verlag: | KIT Scientific Publishing, Karlsruhe |
| Veranstaltungstitel: | Forum Bildverarbeitung 2016 |
| Veranstaltungsort: | Karlsruhe |
| Veranstaltungsdatum: | 01.-02. Dezember 2016 |
| Kurzbeschreibung (Abstract): | Corneal confocal microscopy is a promising diagnostic method for peripheral neuropathy. It allows the recording of the sub-basal nerve plexus (SNP) and enables the morphological analysis of peripheral nerves. This work evaluates classification models for real-time evaluation of cornea images in order to find suitable methods for an automatic focus adaptation to the SNP. The analyzed Bag-of-Visual-Words method leads to models with an accuracy of 0.9, even on a small training dataset, and a runtime of 18 ms per image. Furthermore, the analysis of the support vector machine real-valued output shows high potential for the implementation of real-time focus optimization methods. |
| Freie Schlagworte: | Guiding Theme: Individual Health, Research Area: Computer vision (CV), Image classification, Image processing |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 08 Mai 2019 06:31 |
| Letzte Änderung: | 08 Mai 2019 06:31 |
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