Hyperspectral characterization of re-epithelialization in an in vitro wound model

被引:6
作者
Bjorgan, Asgeir [1 ]
Pukstad, Brita S. [2 ,3 ]
Randeberg, Lise L. [1 ]
机构
[1] NTNU Norwegian Univ Sci & Technol, Dept Elect Syst, Trondheim, Norway
[2] NTNU Norwegian Univ Sci & Technol, Dept Clin & Mol Med, Trondheim, Norway
[3] Trondheim Reg & Univ Hosp, St Olavs Hosp, Dept Dermatol, Trondheim, Norway
关键词
exploratory data analysis; hyperspectral imaging; image processing; photon transport modeling; re-epithelialization; tissue optics; wound healing; HUMAN SKIN; DIFFUSE-REFLECTANCE; AMNIOTIC-FLUID; RANDOM FOREST; REEPITHELIALIZATION; CLASSIFICATION;
D O I
10.1002/jbio.202000108
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
In vitro wound models are useful for research on wound re-epithelialization. Hyperspectral imaging represents a non-destructive alternative to histology analysis for detection of re-epithelialization. This study aims to characterize the main optical behavior of a wound model in order to enable development of detection algorithms.K-Means clustering and agglomerative analysis were used to group spatial regions based on the spectral behavior, and an inverse photon transport model was used to explain differences in optical properties. Six samples of the wound model were prepared from human tissue and followed over 22 days. Re-epithelialization occurred at a mean rate of 0.24 mm(2)/day after day 8 to 10. Suppression of wound spectral features was the main feature characterizing re-epithelialized and intact tissue. Modeling the photon transport through a diffuse layer placed on top of wound tissue properties reproduced the spectral behavior. The missing top layer represented by wounds is thus optically detectable using hyperspectral imaging.
引用
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页数:17
相关论文
共 56 条
[1]  
[Anonymous], THESIS
[2]  
Arnoux V, 2005, MOL B INT U, P111
[3]   Hyperspectral imaging for tissue classification, a way toward smart laparoscopic colorectal surgery [J].
Baltussen, Elisabeth J. M. ;
Kok, Esther N. D. ;
de Koning, Susan G. Brouwer ;
Sanders, Joyce ;
Aalbers, Arend G. J. ;
Kok, Niels F. M. ;
Beets, Geerard L. ;
Flohil, Claudie C. ;
Bruin, Sjoerd C. ;
Kuhlmann, Koert F. D. ;
Sterenborg, Henricus J. C. M. ;
Ruers, Theo J. M. .
JOURNAL OF BIOMEDICAL OPTICS, 2019, 24 (01)
[4]   Spectral Derivative Features for Classification of Hyperspectral Remote Sensing Images: Experimental Evaluation [J].
Bao, Jiangfeng ;
Chi, Mingmin ;
Benediktsson, Jon Atli .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2013, 6 (02) :594-601
[5]   Random forest in remote sensing: A review of applications and future directions [J].
Belgiu, Mariana ;
Dragut, Lucian .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2016, 114 :24-31
[6]   A random forest-based method for selection of regions of interest in hyperspectral images of ex vivo human skin [J].
Bjorgan, Asgeir ;
Randeberg, Lise L. .
HIGH-SPEED BIOMEDICAL IMAGING AND SPECTROSCOPY IV, 2019, 10889
[7]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[8]   Chromophore based analyses of steady-state diffuse reflectance spectroscopy: current status and perspectives for clinical adoption [J].
Bydlon, Torre M. ;
Nachabe, Rami ;
Ramanujam, Nimmi ;
Sterenborg, Henricus J. C. M. ;
Hendriks, Benno H. W. .
JOURNAL OF BIOPHOTONICS, 2015, 8 (1-2) :9-24
[9]  
Calin M. A., 2019, MIUA 2019 MED IMAGE, P74
[10]   Hyperspectral imaging-based wound analysis using mixture-tuned matched filtering classification method [J].
Calin, Mihaela Antonina ;
Coman, Toma ;
Parasca, Sorin Viorel ;
Bercaru, Nicolae ;
Savastru, Roxana ;
Manea, Dragos .
JOURNAL OF BIOMEDICAL OPTICS, 2015, 20 (04)