Tissue classification for laparoscopic image understanding based on multispectral texture analysis

Yan Zhang; Sebastian J. Wirkert; Justin Iszatt; Hannes Kenngott; Martin Wagner; Benjamin Mayer; Christian Stock; Neil T. Clancy; Daniel S. Elson; Lena Maier-Hein

doi:10.1117/12.2216090

18 March 2016 Tissue classification for laparoscopic image understanding based on multispectral texture analysis

Yan Zhang, Sebastian J. Wirkert, Justin Iszatt, Hannes Kenngott, Martin Wagner, Benjamin Mayer, Christian Stock, Neil T. Clancy, Daniel S. Elson, Lena Maier-Hein

Author Affiliations +

Proceedings Volume 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling; 978619 (2016) https://doi.org/10.1117/12.2216090
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Yan Zhang, Sebastian J. Wirkert, Justin Iszatt, Hannes Kenngott, Martin Wagner, Benjamin Mayer, Christian Stock, Neil T. Clancy, Daniel S. Elson, and Lena Maier-Hein "Tissue classification for laparoscopic image understanding based on multispectral texture analysis", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 978619 (18 March 2016); https://doi.org/10.1117/12.2216090

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