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Proceedings Paper

Tissue classification for laparoscopic image understanding based on multispectral texture analysis
Author(s): Yan Zhang; Sebastian J. Wirkert; Justin Iszatt; Hannes Kenngott; Martin Wagner; Benjamin Mayer; Christian Stock; Neil T. Clancy; Daniel S. Elson; Lena Maier-Hein
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Paper 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.

Paper Details

Date Published: 18 March 2016
PDF: 10 pages
Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 978619 (18 March 2016); doi: 10.1117/12.2216090
Show Author Affiliations
Yan Zhang, German Cancer Research Ctr. (Germany)
Sebastian J. Wirkert, German Cancer Research Ctr. (Germany)
Justin Iszatt, German Cancer Research Ctr. (Germany)
Hannes Kenngott, Heidelberg Univ. Hospital (Germany)
Martin Wagner, Heidelberg Univ. Hospital (Germany)
Benjamin Mayer, Heidelberg Univ. Hospital (Germany)
Christian Stock, Institute of Medical Biometry and Informatics, Univ. of Heidelberg (Germany)
Neil T. Clancy, Institute of Global Health Innovation, Imperial College London (United Kingdom)
Daniel S. Elson, Institute of Global Health Innovation, Imperial College London (United Kingdom)
Lena Maier-Hein, German Cancer Research Ctr. (Germany)


Published in SPIE Proceedings Vol. 9786:
Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster; Ziv R. Yaniv, Editor(s)

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