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Open AccessArticle

Investigation of the Performance of Hyperspectral Imaging by Principal Component Analysis in the Prediction of Healing of Diabetic Foot Ulcers

1
Optics and Photonics Research Group, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK
2
Biomedical Information Processing Lab, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
3
Nottingham University Hospitals NHS Trust, Nottingham NG5 1PB, UK
4
University Hospitals of Derby and Burton NHS Foundation Trust, Derby DE22 3NE, UK
*
Author to whom correspondence should be addressed.
J. Imaging 2018, 4(12), 144; https://doi.org/10.3390/jimaging4120144
Received: 22 September 2018 / Revised: 1 December 2018 / Accepted: 4 December 2018 / Published: 7 December 2018
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
Diabetic foot ulcers are a major complication of diabetes and present a considerable burden for both patients and health care providers. As healing often takes many months, a method of determining which ulcers would be most likely to heal would be of great value in identifying patients who require further intervention at an early stage. Hyperspectral imaging (HSI) is a tool that has the potential to meet this clinical need. Due to the different absorption spectra of oxy- and deoxyhemoglobin, in biomedical HSI the majority of research has utilized reflectance spectra to estimate oxygen saturation (SpO2) values from peripheral tissue. In an earlier study, HSI of 43 patients with diabetic foot ulcers at the time of presentation revealed that ulcer healing by 12 weeks could be predicted by the assessment of SpO2 calculated from these images. Principal component analysis (PCA) is an alternative approach to analyzing HSI data. Although frequently applied in other fields, mapping of SpO2 is more common in biomedical HSI. It is therefore valuable to compare the performance of PCA with SpO2 measurement in the prediction of wound healing. Data from the same study group have now been used to examine the relationship between ulcer healing by 12 weeks when the results of the original HSI are analyzed using PCA. At the optimum thresholds, the sensitivity of prediction of healing by 12 weeks using PCA (87.5%) was greater than that of SpO2 (50.0%), with both approaches showing equal specificity (88.2%). The positive predictive value of PCA and oxygen saturation analysis was 0.91 and 0.86, respectively, and a comparison by receiver operating characteristic curve analysis revealed an area under the curve of 0.88 for PCA compared with 0.66 using SpO2 analysis. It is concluded that HSI may be a better predictor of healing when analyzed by PCA than by SpO2. View Full-Text
Keywords: hyperspectral imaging; principal component analysis; oxygen saturation; wound healing; diabetic foot ulcer hyperspectral imaging; principal component analysis; oxygen saturation; wound healing; diabetic foot ulcer
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MDPI and ACS Style

Yang, Q.; Sun, S.; Jeffcoate, W.J.; Clark, D.J.; Musgove, A.; Game, F.L.; Morgan, S.P. Investigation of the Performance of Hyperspectral Imaging by Principal Component Analysis in the Prediction of Healing of Diabetic Foot Ulcers. J. Imaging 2018, 4, 144. https://doi.org/10.3390/jimaging4120144

AMA Style

Yang Q, Sun S, Jeffcoate WJ, Clark DJ, Musgove A, Game FL, Morgan SP. Investigation of the Performance of Hyperspectral Imaging by Principal Component Analysis in the Prediction of Healing of Diabetic Foot Ulcers. Journal of Imaging. 2018; 4(12):144. https://doi.org/10.3390/jimaging4120144

Chicago/Turabian Style

Yang, Qian; Sun, Shen; Jeffcoate, William J.; Clark, Daniel J.; Musgove, Alison; Game, Fran L.; Morgan, Stephen P. 2018. "Investigation of the Performance of Hyperspectral Imaging by Principal Component Analysis in the Prediction of Healing of Diabetic Foot Ulcers" J. Imaging 4, no. 12: 144. https://doi.org/10.3390/jimaging4120144

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