Tissue classification for laparoscopic image understanding based on multispectral texture analysis
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
Intra-operative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study we show (1) that multispectral imaging data is superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) that combining the tissue texture with the reflectance spectrum improves the classification performance. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.
Details
Originalsprache | Englisch |
---|---|
Titel | Medical Imaging 2016 |
Redakteure/-innen | Robert J. Webster, Ziv R. Yaniv |
Herausgeber (Verlag) | SPIE - The international society for optics and photonics, Bellingham |
ISBN (elektronisch) | 9781510600218 |
Publikationsstatus | Veröffentlicht - 2016 |
Peer-Review-Status | Ja |
Extern publiziert | Ja |
Publikationsreihe
Reihe | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
---|---|
Band | 9786 |
ISSN | 1605-7422 |
Konferenz
Titel | Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling |
---|---|
Dauer | 28 Februar - 1 März 2016 |
Stadt | San Diego |
Land | USA/Vereinigte Staaten |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- multispectral laparoscopy, multispectral texture analysis, tissue classification