Research Papers

Hyperspectral Imaging With Burn Contour Extraction for Burn Wound Depth Assessment

[+] Author and Article Information
Houzhu Ding

Stevens Institute of Technology,
1 Castle Point on Hudson,
Hoboken, NJ 07470
e-mail: hding4@stevens.edu

Robert C. Chang

Stevens Institute of Technology,
1 Castle Point on Hudson,
Hoboken, NJ 07470
e-mail: rchang6@stevens.edu

1Corresponding author.

Manuscript received March 9, 2018; final manuscript received May 29, 2018; published online July 3, 2018. Assoc. Editor: Douglas Dow.

ASME J of Medical Diagnostics 1(4), 041002 (Jul 03, 2018) (10 pages) Paper No: JESMDT-18-1016; doi: 10.1115/1.4040470 History: Received March 09, 2018; Revised May 29, 2018

Skin thermal burn wounds are classified according to subjective assessments of wound depth that indicate divergent modes of medical intervention. However, clinically discriminating superficial partial from deep partial thickness burns remains a significant challenge, where only the latter requires excision and skin grafting. Motivated by the need for and ramifications of an objective burn wound assessment tool, this paper advances hyperspectral imaging (HSI) in a porcine skin burn model to quantitatively evaluate thermal burn injuries (superficial and deep partial thickness burns). Two-dimensional (2D) principal component analysis for noise reduction is applied to images captured by HSI in the visible wavelength range. Herein, a multivariate regression analysis is used to calculate the total hemoglobin concentration (tHb) and the oxygen saturation (StO2) of the injured tissue. These perfusion profiles are spatially mapped to yield characteristic distributions corresponding to the burn wound degree validated histologically. The results demonstrate that StO2 and tHb diverge significantly for superficial partial and deep partial burns at 24 h and 1 h, respectively (p < 0.05). A StO2 burn map at 1 h post-burn yields a 2D burn contour that is registered with a burn color image. This early stage burn-specific contour has implications to guide downstream burn excision and grafting.

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Devgan, L. , Bhat, S. , Aylward, S. , and Spence, R. J. , 2006, “ Modalities for the Assessment of Burn Wound Depth,” J. Burns Wounds, 5, p. e2. http://europepmc.org/articles/PMC1687143 [PubMed]
Crouzet, C. , Nguyen, J. Q. , Ponticorvo, A. , Bernal, N. P. , Durkin, A. J. , and Choi, B. , 2015, “ Acute Discrimination Between Superficial-Partial and Deep-Partial Thickness Burn Injuries in a Preclinical Model With Laser Speckle Imaging,” Burns, 41(5), pp. 1058–1063. [CrossRef] [PubMed]
Chin, M. S. , Babchenko, O. , Lujan-Hernandez, J. , Nobel, L. , Ignotz, R. , and Lalikos, J. F. , 2015, “ Hyperspectral Imaging for Burn Depth Assessment in an Animal Model,” Plast. Reconstr. Surg. - Glob. Open, 3(12), p. e591. [CrossRef] [PubMed]
Cotran, R. S. , and Remensnyder, J. P. , 1968, “ The Structural Basis of Increased Vascular Permeability After Graded Thermal Injury-Light and Electron Microscopic Studies*,” Ann. N. Y. Acad. Sci., 150(3 Early Treatme), pp. 495–509. [CrossRef] [PubMed]
Sowa, M. G. , Leonardi, L. , Payette, J. R. , Fish, J. S. , and Mantsch, H. H. , 2001, “ Near Infrared Spectroscopic Assessment of Hemodynamic Changes in the Early Post-Burn Period,” Burns, 27(3), pp. 241–249. [CrossRef] [PubMed]
Cross, K. M. , Leonardi, L. , Payette, J. R. , Gomez, M. , Levasseur, M. A. , Schattka, B. J. , Sowa, M. G. , and Fish, J. S. , 2007, “ Clinical Utilization of Near-Infrared Spectroscopy Devices for Burn Depth Assessment,” Wound Repair Regen, 15(3), pp. 332–340. [CrossRef] [PubMed]
Park, D. , Hwang, J. , Jang, K. , Han, D. , Ahn, K. , and Baik, B. , 1998, “ Use of Laser Doppler Flowmetry for Estimation of the Depth of Burns,” Plast. Reconstr. Surg., 101(6), pp. 1516–1523.
Iftimia, N. , Ferguson, R. D. , Mujat, M. , Patel, A. H. , Zhang, E. Z. , Fox, W. , and Rajadhyaksha, M. , 2013, “ Combined Reflectance Confocal Microscopy/Optical Coherence Tomography Imaging for Skin Burn Assessment,” Biomed. Opt. Express, 4(5), pp. 680–695. [CrossRef] [PubMed]
Kamruzzaman, M. , Elmasry, G. , Sun, D. W. , and Allen, P. , 2012, “ Non-Destructive Prediction and Visualization of Chemical Composition in Lamb Meat Using NIR Hyperspectral Imaging and Multivariate Regression,” Innov. Food Sci. Emerg. Technol., 16, pp. 218–226. [CrossRef]
Zhang, H. F. , Maslov, K. , Stoica, G. , and Wang, L. V. , 2006, “ Imaging Acute Thermal Burns by Photoacoustic Microscopy,” J. Biomed. Opt., 11(5), pp. 54033–54035. [CrossRef]
Attas, M. , 2006, “ Functional Infrared Imaging for Biomedical Applications,” Handbook of Vibrational Spectroscopy, Wiley, Hoboken, NJ.
Leonardi, L. , Sowa, M. G. , Payette, J. R. , and Mantsch, H. H. , 2000, “ Near-Infrared Spectroscopy and Imaging: A New Approach to Assess Burn Injuries,” Am. Clin. Lab., 19(8), pp. 20–22. http://cdn.metricmarketing.ca/www.machinevision.ca/files/Assessing_Burn_Injuries.pdf?this=that [PubMed]
Seki, T. , Fujioka, M. , Fukushima, H. , Matsumori, H. , Maegawa, N. , Norimoto, K. , and Okuchi, K. , 2014, “ Regional Tissue Oxygen Saturation Measured by Near-Infrared Spectroscopy to Assess the Depth of Burn Injuries,” Int. J. Burns Trauma, 4(1), pp. 40–44. http://europepmc.org/articles/PMC3945827 [PubMed]
Maini, R. , and Aggarwal, H. , 2009, “ Study and Comparison of Various Image Edge Detection Techniques,” Int. J. Image Process., 3(1), pp. 1–11. http://www.cscjournals.org/library/manuscriptinfo.php?mc=IJIP-15
Landgrebe, D. , 2002, “ Hyperspectral Image Data Analysis,” IEEE Signal Process. Mag., 19(1), pp. 17–28. [CrossRef]
Harsanyi, J. C. , and Chang, C. I. , 1994, “ Hyperspectral Image Classification and Dimensionality Reduction: An Orthogonal Subspace Projection Approach,” IEEE Trans. Geosci. Remote Sens., 32(4), pp. 779–785. [CrossRef]
Zuzak, K. J. , Schaeberle, M. D. , Lewis, E. N. , and Levin, I. W. , 2002, “ Visible Reflectance Hyperspectral Imaging: Characterization of a Noninvasive, In Vivo System for Determining Tissue Perfusion,” Anal. Chem., 74(9), p. 2021. [CrossRef] [PubMed]
Alexopoulos, E. C. , 2010, “ Introduction to Multivariate Regression Analysis,” Hippokratia, 14(Suppl. 1), pp. 23–28. http://europepmc.org/articles/PMC3049417 [PubMed]
Chin, M. S. , Freniere, B. B. , Bonney, C. F. , Lancerotto, L. , Saleeby, J. H. , Lo, Y.-C. , Orgill, D. P. , Fitzgerald, T. J. , and Lalikos, J. F. , 2013, “ Skin Perfusion and Oxygenation Changes in Radiation Fibrosis,” Plast. Reconstr. Surg., 131(4), pp. 707–716.
Chin, M. S. , Freniere, B. B. , Lo, Y.-C. , Saleeby, J. H. , Baker, S. P. , Strom, H. M. , Ignotz, R. A. , Lalikos, J. F. , and Fitzgerald, T. J. , 2012, “ Hyperspectral Imaging for Early Detection of Oxygenation and Perfusion Changes in Irradiated Skin,” J. Biomed. Opt., 17(2), p. 026010. [CrossRef] [PubMed]
Ding, H. , and Chang, R. C. , 2015, “ Comparison of Photometric Stereo and Spectral Analysis for Visualization and Assessment of Burn Injury From Hyperspectral Imaging,” IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Shenzhen, China, pp. 1–6.
Singer, A. J. , Berruti, L. , Thode, H. C. , and McClain, S. A. , 2000, “ Standardized Burn Model Using a Multiparametric Histologic Analysis of Burn Depth,” Acad. Emerg. Med., 7(1), pp. 1–6. [CrossRef] [PubMed]
Dwyer, P. J. , Anderson, R. R. , and DiMarzio, C. A. , 1997, “ Mapping Blood Oxygen Saturation Using a Multispectral Imaging System,” Proc. SPIE, 2976, pp. 2911–2976. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/2976/1/Mapping-blood-oxygen-saturation-using-a-multispectral-imaging-system/10.1117/12.275535.short?SSO=1
Subramanian, N. R. , Kerekes, J. P. , Kearney, K. , and Schad, N. , 2006, “ Spectral Imaging of Near-Surface Oxygen Saturation,” Proc. SPIE, 6142, p. 61423Y(1-9). https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6142/1/Spectral-imaging-of-near-surface-oxygen-saturation/10.1117/12.655699.short
Velasquillo, C. , 2013, “ Skin 3D Bioprinting. Applications in Cosmetology,” J. Cosmet. Dermatol. Sci. Appl., 3(1), pp. 85–89. http://www.oalib.com/paper/286038#.Wx2HGkgvyiM
Soille, P. , 2013, Morphological Image Analysis: Principles and Applications, Springer Science & Business Media, New York.
Gonzalez, R. C. , and Woods, R. E. , 2012, Digital Image Processing, Pearson, London.
Hardy, J. D. , Hammel, H. T. , and Murgatroyd, D. , 1956, “ Spectral Transmittance and Reflectance of Excised Human Skin,” J. Appl. Physiol., 9(2), pp. 257–264. [CrossRef] [PubMed]
Smith, A. M. , Mancini, M. C. , and Nie, S. , 2009, “ Bioimaging: Second Window for In Vivo Imaging,” Nat. Nanotechnol., 4(11), pp. 710–711. [CrossRef] [PubMed]
Lee, Y. , and Hwang, K. , 2002, “ Skin Thickness of Korean Adults,” Surg. Radiol. Anat., 24(3–4), pp. 183–189. https://rd.springer.com/article/10.1007/s00276-002-0034-5 [PubMed]
Ding, H. , and Chang, R. C. , 2018, “ Printability Study of Bioprinted Tubular Structures Using Liquid Hydrogel Precursors in a Support Bath,” Appl. Sci., 8(3), p. 403. [CrossRef]
Ding, H. , Tourlomousis, F. , and Chang, R. C. , 2017, “ Bioprinting Multidimensional Constructs: A Quantitative Approach to Understanding Printed Cell Density and Redistribution Phenomena,” Biomed. Phys. Eng. Express, 3(3), p. 35016. [CrossRef]
Ding, H. , Tourlomousis, F. , and Chang, R. C. , 2018, “ A Methodology for Quantifying Cell Density and Distribution in Multidimensional Bioprinted Gelatin-Alginate Constructs,” ASME J. Manuf. Sci. Eng., 140(5), p. 051014. [CrossRef]
Bradshaw, M. , Ho, D. , Fear, M. W. , Gelain, F. , Wood, F. M. , and Iyer, K. S. , 2014, “ Designer Self-Assembling Hydrogel Scaffolds Can Impact Skin Cell Proliferation and Migration,” Sci. Rep., 4, p. 6903. [CrossRef] [PubMed]
Lee, V. , Singh, G. , Trasatti, J. P. , Bjornsson, C. , Xu, X. , Tran, T. N. , Yoo, S.-S. , Dai, G. , and Karande, P. , 2014, “ Design and Fabrication of Human Skin by Three-Dimensional Bioprinting,” Tissue Eng. Part C. Methods, 20(6), pp. 473–484. [CrossRef] [PubMed]
Carolina, N. , 2012, “ Bioprinted Amniotic Fluid-Derived Stem Cells Accelerate Healing of Large Skin Wounds,” Stem Cells Transl. Med., 1(11), pp. 792–802. [CrossRef] [PubMed]
Ding, H. , Dole, A. , Tourlomousis, F. , and Chang, R. C. , 2016, “ Design of a Skin Grafting Methodology for Burn Wound Using an Additive Biomanufacturing System Guided by Hyperspectral Imaging,” ASME Paper No. MSEC2016-8588.
Ng, W. L. , Wang, S. , Yeong, W. Y. , and Naing, M. W. , 2017, “ Skin Bioprinting: Impending Reality or Fantasy?,” Trends Biotechnol., 34(9), pp. 689–699. [CrossRef]


Grahic Jump Location
Fig. 1

Spectral information of three types of skin (normal, superficial partial burn, and deep partial burn) with 128 bands. The areas with blue background represent the selected band range without significant noise (from bands 10 to 90). For a selected ROI, 100 pixels are randomly chosen and sorted with a band range of 1:128. For each band, the absolute difference between adjacent pixels is computed. The sum of the difference is plotted as the lower graphs displayed in ((a)–(c)), where the maximum difference is 0.1138 for normal skin, 0.1599 for superficial partial skin, 0.0896 for deep partial burn, which are less than 0.2 (the global threshold for all HSI images).

Grahic Jump Location
Fig. 3

Histological analysis (a) denotes the dermal parameters that can be applied to evaluate the burn depth. 1 = depth of mesenchymal cell necrosis; 2 = depth of follicular cell necrosis; 3a = bluish discoloration of collagen; 3b = depth of intercollagen bluish material; 4 = depth of vascular endothelial cell necrosis. (b) shows statistical significance that samples A, B diverge from samples C, D with respect to burn depth. (c) and (d) highlight the change in three key dermal parameters over time in a deep partial burn (samples A and B) versus a superficial partial burn (samples C and D), respectively.

Grahic Jump Location
Fig. 4

HSI image of four burn samples, denoted as A, B, C, and D, where A, B denote the deep partial burn wounds, and C, D are superficial burn wounds. Images are captured at four different time points: T0, T1, T4, and T24. Punch biopsy is implemented at the edge and center area to acquire histological sample data.

Grahic Jump Location
Fig. 5

tHb, HbO2, and Hb map of superficial partial and deep partial burns. (a) denotes the superficial partial burn and (b) denotes the deep partial burn. The burn regions are cropped intentionally to avoid the punch biopsy to avoid local maximum of hemoglobin. Data from (c)–(e) are carefully selected around the punch biopsy site to correlate the histological data with the image data.

Grahic Jump Location
Fig. 6

StO2 map of superficial partial and deep partial burns. (a) denotes the superficial partial burn and (b) denotes the deep partial burn. The overall burn sites are selected to show the overview of the whole burn region, including burn, normal and biopsy site. Data from (c) are based on regions of interest near the punch biopsy site to correlate the histological data with the image data.

Grahic Jump Location
Fig. 7

Extracted deep burn contour and registered with color burn image. This workflow shows a deep burn depth map derived from StO2. The StO2 contour includes both the burn region and the punch biopsy region where, based on the multivariate analysis, the StO2 concentration derived from biopsy region also affect the final overall StO2 map.

Grahic Jump Location
Fig. 2

Workflow of the image processing step (a) is a summary illustrating the workflow for the entire image processing steps. (b) is a schematic showing how to compute and connect the nearest neighbors within a prescribed distance. (c) shows the four phases during the image processing procedure where (c-i) is the unsmooth binary image, (c-ii) shows the extracted key points, (c-iii) shows the new reconstructed smooth boundary by connecting thenearest neighbors, and (c-iv) is the final smoothed binary image.



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