Tissue classification for laparoscopic image understanding based on multispectral texture analysis

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

doi:10.1117/1.JMI.4.1.015001

25 January 2017 Tissue classification for laparoscopic image understanding based on multispectral texture analysis

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

Author Affiliations +

J. of Medical Imaging, 4(1), 015001 (2017). https://doi.org/10.1117/1.JMI.4.1.015001

Abstract

Intraoperative 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 through statistical analysis, we show that (1) multispectral imaging data are superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) combining the tissue texture with the reflectance spectrum improves the classification performance. The classifier reaches an accuracy of 98.4% on our dataset. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

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Yan Zhang, Yan Zhang, Sebastian Wirkert, Sebastian Wirkert, Justin Iszatt, Justin Iszatt, Hannes Kenngott, Hannes Kenngott, Martin Wagner, Martin Wagner, Benjamin Mayer, Benjamin Mayer, Christian Stock, Christian Stock, Neil T. Clancy, Neil T. Clancy, Daniel S. Elson, Daniel S. Elson, Lena Maier-Hein, Lena Maier-Hein, "Tissue classification for laparoscopic image understanding based on multispectral texture analysis," Journal of Medical Imaging 4(1), 015001 (25 January 2017). https://doi.org/10.1117/1.JMI.4.1.015001

Received: 5 June 2016; Accepted: 16 December 2016; Published: 25 January 2017

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