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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

Mem. ASME
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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Figures

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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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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).

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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.

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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.

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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.

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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.

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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.

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