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@inproceedings{Zhang:2016:10.1117/12.2216090,
author = {Zhang, Y and Wirkert, SJ and Iszatt, J and Kenngott, H and Wagner, M and Mayer, B and Stock, C and Clancy, NT and Elson, DS and Maier-Hein, L},
doi = {10.1117/12.2216090},
publisher = {Society of Photo-optical Instrumentation Engineers (SPIE)},
title = {Tissue classification for laparoscopic image understanding based on multispectral texture analysis},
url = {http://dx.doi.org/10.1117/12.2216090},
year = {2016}
}

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TY  - CPAPER
AB  - 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.
AU  - Zhang,Y
AU  - Wirkert,SJ
AU  - Iszatt,J
AU  - Kenngott,H
AU  - Wagner,M
AU  - Mayer,B
AU  - Stock,C
AU  - Clancy,NT
AU  - Elson,DS
AU  - Maier-Hein,L
DO  - 10.1117/12.2216090
PB  - Society of Photo-optical Instrumentation Engineers (SPIE)
PY  - 2016///
SN  - 1996-756X
TI  - Tissue classification for laparoscopic image understanding based on multispectral texture analysis
UR  - http://dx.doi.org/10.1117/12.2216090
UR  - http://hdl.handle.net/10044/1/41490
ER  -

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Dr Neil T Clancy

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