Tissue classification for laparoscopic image understanding based on multispectral texture analysis
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 m...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
25 January 2017
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| In: |
Journal of medical imaging
Year: 2017, Volume: 4, Issue: 1, Pages: 4 |
| ISSN: | 2329-4310 |
| DOI: | 10.1117/1.JMI.4.1.015001 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1117/1.JMI.4.1.015001 Verlag, Volltext: https://www.spiedigitallibrary.org/journals/Journal-of-Medical-Imaging/volume-4/issue-1/015001/Tissue-classification-for-laparoscopic-image-understanding-based-on-multispectral-texture/10.1117/1.JMI.4.1.015001.short 
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| Author Notes: | Yan Zhang, Sebastian Wirkert, Justin Iszatt, Hannes Kenngott, Martin Wagner, Benjamin Mayer, Christian Stock, Neil T. Clancy, Daniel S. Elson, Lena Maier-Hein |
| Summary: | 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 <italic>ex vivo</italic> 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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| Item Description: | Gesehen am 01.08.2018 |
| Physical Description: | Online Resource |
| ISSN: | 2329-4310 |
| DOI: | 10.1117/1.JMI.4.1.015001 |