Abstract

The process of burn debridement is a challenging technique requiring significant skills to identify the regions that need excision and their appropriate excision depths. In order to assist surgeons, a machine learning tool is being developed to provide a quantitative assessment of burn-injured tissue. This paper presents three non-invasive optical imaging techniques capable of distinguishing four kinds of tissue—healthy skin, viable wound bed, shallow burn, and deep burn—during serial burn debridement in a porcine model. All combinations of these three techniques have been studied through a k-fold cross-validation method. In terms of global performance, the combination of all three techniques significantly improves the classification accuracy with respect to just one technique, from 0.42 up to more than 0.76. Furthermore, a non-linear spatial filtering based on the mode of a small neighborhood has been applied as a post-processing technique, in order to improve the performance of the classification. Using this technique, the global accuracy reaches a value close to 0.78 and, for some particular tissues and combination of techniques, the accuracy improves by 13%.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  1. D. P. Orgill, “Excision and skin grafting of thermal burns,” New England Journal of Medicine 360, 893–901 (2009).
    [Crossref] [PubMed]
  2. R. Gurfinkel, L. Rosenberg, S. Cohen, A. Cohen, A. Barezovsky, E. Cagnano, and A. J. Singer, “Histological assessment of tangentially excised burn eschars,” The Canadian Journal of Plastic Surgery 18, e33 (2010).
    [Crossref]
  3. L. Devgan, S. Bhat, S. Aylward, and R. J. Spence, “Modalities for the assessment of burn wound depth,” Journal of Burns and Wounds 5, e2 (2006).
    [PubMed]
  4. J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
    [Crossref] [PubMed]
  5. D. M. Burmeister, A. Ponticorvo, B. Yang, S. C. Becerra, B. Choi, A. J. Durkin, and R. J. Christy, “Utility of spatial frequency domain imaging (sfdi) and laser speckle imaging (lsi) to non-invasively diagnose burn depth in a porcine model,” Burns 41, 1242–1252 (2015).
    [Crossref] [PubMed]
  6. S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
    [Crossref]
  7. J. Heredia-Juesas, J. E. Thatcher, Y. Lu, J. J. Squiers, D. R. King, W. Fan, J. M. DiMaio, and J. A. Martinez-Lorenzo, “Non-invasive optical imaging techniques for burn-injured tissue detection for debridement surgery,”in International Conference of the IEEE Engineering in Medicine and Biology Society (2016).
  8. J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
    [Crossref]
  9. J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
    [Crossref]
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    [Crossref]
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    [Crossref] [PubMed]
  14. D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
    [Crossref] [PubMed]
  15. J. E. Thatcher, K. D. Plant, D. R. King, K. L. Block, W. Fan, and J. M. DiMaio, “Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device,” in SPIE Sensing Technology+ Applications (International Society for Optics and Photonics, 2014), pp. 910718–910718.
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    [Crossref]

2016 (2)

J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
[Crossref] [PubMed]

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

2015 (4)

W. Li, W. Mo, X. Zhang, J. J. Squiers, Y. Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” J. Biomed. Opt. 20, 121305 (2015).
[Crossref] [PubMed]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

D. M. Burmeister, A. Ponticorvo, B. Yang, S. C. Becerra, B. Choi, A. J. Durkin, and R. J. Christy, “Utility of spatial frequency domain imaging (sfdi) and laser speckle imaging (lsi) to non-invasively diagnose burn depth in a porcine model,” Burns 41, 1242–1252 (2015).
[Crossref] [PubMed]

S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
[Crossref]

2013 (1)

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

2010 (1)

R. Gurfinkel, L. Rosenberg, S. Cohen, A. Cohen, A. Barezovsky, E. Cagnano, and A. J. Singer, “Histological assessment of tangentially excised burn eschars,” The Canadian Journal of Plastic Surgery 18, e33 (2010).
[Crossref]

2009 (1)

D. P. Orgill, “Excision and skin grafting of thermal burns,” New England Journal of Medicine 360, 893–901 (2009).
[Crossref] [PubMed]

2006 (1)

L. Devgan, S. Bhat, S. Aylward, and R. J. Spence, “Modalities for the assessment of burn wound depth,” Journal of Burns and Wounds 5, e2 (2006).
[PubMed]

1976 (1)

R. L. Kettig and D. Landgrebe, “Classification of multispectral image data by extraction and classification of homogeneous objects,” IEEE Transactions on Geoscience Electronics 14, 19–26 (1976).
[Crossref]

Avci, P.

P. Avci, A. Gupta, M. Sadasivam, D. Vecchio, Z. Pam, N. Pam, and M. R. Hamblin, “Low-level laser (light) therapy (lllt) in skin: stimulating, healing, restoring,” in Seminars in Cutaneous Medicine and Surgery, vol. 32 (Frontline Medical Communications, 2013), pp. 41–52.

Aylward, S.

L. Devgan, S. Bhat, S. Aylward, and R. J. Spence, “Modalities for the assessment of burn wound depth,” Journal of Burns and Wounds 5, e2 (2006).
[PubMed]

Barezovsky, A.

R. Gurfinkel, L. Rosenberg, S. Cohen, A. Cohen, A. Barezovsky, E. Cagnano, and A. J. Singer, “Histological assessment of tangentially excised burn eschars,” The Canadian Journal of Plastic Surgery 18, e33 (2010).
[Crossref]

Barolet, D.

D. Barolet, “Light-emitting diodes (leds) in dermatology,” in Seminars in cutaneous medicine and surgery, vol. 27 (Frontline Medical Communications, 2008), pp. 227–238.
[Crossref]

Becerra, S. C.

D. M. Burmeister, A. Ponticorvo, B. Yang, S. C. Becerra, B. Choi, A. J. Durkin, and R. J. Christy, “Utility of spatial frequency domain imaging (sfdi) and laser speckle imaging (lsi) to non-invasively diagnose burn depth in a porcine model,” Burns 41, 1242–1252 (2015).
[Crossref] [PubMed]

Bernal, N.

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

Bhat, S.

L. Devgan, S. Bhat, S. Aylward, and R. J. Spence, “Modalities for the assessment of burn wound depth,” Journal of Burns and Wounds 5, e2 (2006).
[PubMed]

Bilenca, A.

S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
[Crossref]

Blaschke, T.

T. Blaschke, D. Tiede, and S. Lang, “An object-based information extraction methodology incorporating a-priori spatial information,” in ESA Conference on Image Information Mining (2006).

Block, K. L.

J. E. Thatcher, K. D. Plant, D. R. King, K. L. Block, W. Fan, and J. M. DiMaio, “Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device,” in SPIE Sensing Technology+ Applications (International Society for Optics and Photonics, 2014), pp. 910718–910718.

Burmeister, D. M.

D. M. Burmeister, A. Ponticorvo, B. Yang, S. C. Becerra, B. Choi, A. J. Durkin, and R. J. Christy, “Utility of spatial frequency domain imaging (sfdi) and laser speckle imaging (lsi) to non-invasively diagnose burn depth in a porcine model,” Burns 41, 1242–1252 (2015).
[Crossref] [PubMed]

Cagnano, E.

R. Gurfinkel, L. Rosenberg, S. Cohen, A. Cohen, A. Barezovsky, E. Cagnano, and A. J. Singer, “Histological assessment of tangentially excised burn eschars,” The Canadian Journal of Plastic Surgery 18, e33 (2010).
[Crossref]

Choi, B.

D. M. Burmeister, A. Ponticorvo, B. Yang, S. C. Becerra, B. Choi, A. J. Durkin, and R. J. Christy, “Utility of spatial frequency domain imaging (sfdi) and laser speckle imaging (lsi) to non-invasively diagnose burn depth in a porcine model,” Burns 41, 1242–1252 (2015).
[Crossref] [PubMed]

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

Christy, R. J.

D. M. Burmeister, A. Ponticorvo, B. Yang, S. C. Becerra, B. Choi, A. J. Durkin, and R. J. Christy, “Utility of spatial frequency domain imaging (sfdi) and laser speckle imaging (lsi) to non-invasively diagnose burn depth in a porcine model,” Burns 41, 1242–1252 (2015).
[Crossref] [PubMed]

Cohen, A.

R. Gurfinkel, L. Rosenberg, S. Cohen, A. Cohen, A. Barezovsky, E. Cagnano, and A. J. Singer, “Histological assessment of tangentially excised burn eschars,” The Canadian Journal of Plastic Surgery 18, e33 (2010).
[Crossref]

Cohen, S.

R. Gurfinkel, L. Rosenberg, S. Cohen, A. Cohen, A. Barezovsky, E. Cagnano, and A. J. Singer, “Histological assessment of tangentially excised burn eschars,” The Canadian Journal of Plastic Surgery 18, e33 (2010).
[Crossref]

Crouzet, C.

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

Devgan, L.

L. Devgan, S. Bhat, S. Aylward, and R. J. Spence, “Modalities for the assessment of burn wound depth,” Journal of Burns and Wounds 5, e2 (2006).
[PubMed]

DiMaio, J. M.

J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
[Crossref] [PubMed]

W. Li, W. Mo, X. Zhang, J. J. Squiers, Y. Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” J. Biomed. Opt. 20, 121305 (2015).
[Crossref] [PubMed]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

J. E. Thatcher, K. D. Plant, D. R. King, K. L. Block, W. Fan, and J. M. DiMaio, “Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device,” in SPIE Sensing Technology+ Applications (International Society for Optics and Photonics, 2014), pp. 910718–910718.

J. Heredia-Juesas, J. E. Thatcher, Y. Lu, J. J. Squiers, D. R. King, W. Fan, J. M. DiMaio, and J. A. Martinez-Lorenzo, “Non-invasive optical imaging techniques for burn-injured tissue detection for debridement surgery,”in International Conference of the IEEE Engineering in Medicine and Biology Society (2016).

Dronov, V.

S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
[Crossref]

Durkin, A. J.

D. M. Burmeister, A. Ponticorvo, B. Yang, S. C. Becerra, B. Choi, A. J. Durkin, and R. J. Christy, “Utility of spatial frequency domain imaging (sfdi) and laser speckle imaging (lsi) to non-invasively diagnose burn depth in a porcine model,” Burns 41, 1242–1252 (2015).
[Crossref] [PubMed]

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

Fan, W.

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

W. Li, W. Mo, X. Zhang, J. J. Squiers, Y. Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” J. Biomed. Opt. 20, 121305 (2015).
[Crossref] [PubMed]

J. E. Thatcher, K. D. Plant, D. R. King, K. L. Block, W. Fan, and J. M. DiMaio, “Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device,” in SPIE Sensing Technology+ Applications (International Society for Optics and Photonics, 2014), pp. 910718–910718.

J. Heredia-Juesas, J. E. Thatcher, Y. Lu, J. J. Squiers, D. R. King, W. Fan, J. M. DiMaio, and J. A. Martinez-Lorenzo, “Non-invasive optical imaging techniques for burn-injured tissue detection for debridement surgery,”in International Conference of the IEEE Engineering in Medicine and Biology Society (2016).

Friedman, J.

J. Friedman, T. Hastie, and R. Tibshirani, The Elements of Statistical Learning, vol. 1, Springer Series in Statistics (Springer2001).

Gupta, A.

P. Avci, A. Gupta, M. Sadasivam, D. Vecchio, Z. Pam, N. Pam, and M. R. Hamblin, “Low-level laser (light) therapy (lllt) in skin: stimulating, healing, restoring,” in Seminars in Cutaneous Medicine and Surgery, vol. 32 (Frontline Medical Communications, 2013), pp. 41–52.

Gurfinkel, R.

R. Gurfinkel, L. Rosenberg, S. Cohen, A. Cohen, A. Barezovsky, E. Cagnano, and A. J. Singer, “Histological assessment of tangentially excised burn eschars,” The Canadian Journal of Plastic Surgery 18, e33 (2010).
[Crossref]

Hamblin, M. R.

P. Avci, A. Gupta, M. Sadasivam, D. Vecchio, Z. Pam, N. Pam, and M. R. Hamblin, “Low-level laser (light) therapy (lllt) in skin: stimulating, healing, restoring,” in Seminars in Cutaneous Medicine and Surgery, vol. 32 (Frontline Medical Communications, 2013), pp. 41–52.

Hastie, T.

J. Friedman, T. Hastie, and R. Tibshirani, The Elements of Statistical Learning, vol. 1, Springer Series in Statistics (Springer2001).

Hazan, S.

S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
[Crossref]

Heredia-Juesas, J.

J. Heredia-Juesas, J. E. Thatcher, Y. Lu, J. J. Squiers, D. R. King, W. Fan, J. M. DiMaio, and J. A. Martinez-Lorenzo, “Non-invasive optical imaging techniques for burn-injured tissue detection for debridement surgery,”in International Conference of the IEEE Engineering in Medicine and Biology Society (2016).

Kanick, S. C.

J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
[Crossref] [PubMed]

Kettig, R. L.

R. L. Kettig and D. Landgrebe, “Classification of multispectral image data by extraction and classification of homogeneous objects,” IEEE Transactions on Geoscience Electronics 14, 19–26 (1976).
[Crossref]

King, D. R.

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
[Crossref] [PubMed]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

J. E. Thatcher, K. D. Plant, D. R. King, K. L. Block, W. Fan, and J. M. DiMaio, “Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device,” in SPIE Sensing Technology+ Applications (International Society for Optics and Photonics, 2014), pp. 910718–910718.

J. Heredia-Juesas, J. E. Thatcher, Y. Lu, J. J. Squiers, D. R. King, W. Fan, J. M. DiMaio, and J. A. Martinez-Lorenzo, “Non-invasive optical imaging techniques for burn-injured tissue detection for debridement surgery,”in International Conference of the IEEE Engineering in Medicine and Biology Society (2016).

Landgrebe, D.

R. L. Kettig and D. Landgrebe, “Classification of multispectral image data by extraction and classification of homogeneous objects,” IEEE Transactions on Geoscience Electronics 14, 19–26 (1976).
[Crossref]

Lang, S.

T. Blaschke, D. Tiede, and S. Lang, “An object-based information extraction methodology incorporating a-priori spatial information,” in ESA Conference on Image Information Mining (2006).

Li, W.

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

W. Li, W. Mo, X. Zhang, J. J. Squiers, Y. Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” J. Biomed. Opt. 20, 121305 (2015).
[Crossref] [PubMed]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

Liaw, L.-H.

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

Lu, Y.

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
[Crossref] [PubMed]

W. Li, W. Mo, X. Zhang, J. J. Squiers, Y. Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” J. Biomed. Opt. 20, 121305 (2015).
[Crossref] [PubMed]

J. Heredia-Juesas, J. E. Thatcher, Y. Lu, J. J. Squiers, D. R. King, W. Fan, J. M. DiMaio, and J. A. Martinez-Lorenzo, “Non-invasive optical imaging techniques for burn-injured tissue detection for debridement surgery,”in International Conference of the IEEE Engineering in Medicine and Biology Society (2016).

Mai, T.

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

Martinez-Lorenzo, J. A.

J. Heredia-Juesas, J. E. Thatcher, Y. Lu, J. J. Squiers, D. R. King, W. Fan, J. M. DiMaio, and J. A. Martinez-Lorenzo, “Non-invasive optical imaging techniques for burn-injured tissue detection for debridement surgery,”in International Conference of the IEEE Engineering in Medicine and Biology Society (2016).

Mo, W.

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

W. Li, W. Mo, X. Zhang, J. J. Squiers, Y. Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” J. Biomed. Opt. 20, 121305 (2015).
[Crossref] [PubMed]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

Mohan, R.

J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
[Crossref] [PubMed]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

Nguyen, J. Q.

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

Orgill, D. P.

D. P. Orgill, “Excision and skin grafting of thermal burns,” New England Journal of Medicine 360, 893–901 (2009).
[Crossref] [PubMed]

Pam, N.

P. Avci, A. Gupta, M. Sadasivam, D. Vecchio, Z. Pam, N. Pam, and M. R. Hamblin, “Low-level laser (light) therapy (lllt) in skin: stimulating, healing, restoring,” in Seminars in Cutaneous Medicine and Surgery, vol. 32 (Frontline Medical Communications, 2013), pp. 41–52.

Pam, Z.

P. Avci, A. Gupta, M. Sadasivam, D. Vecchio, Z. Pam, N. Pam, and M. R. Hamblin, “Low-level laser (light) therapy (lllt) in skin: stimulating, healing, restoring,” in Seminars in Cutaneous Medicine and Surgery, vol. 32 (Frontline Medical Communications, 2013), pp. 41–52.

Plant, K. D.

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

J. E. Thatcher, K. D. Plant, D. R. King, K. L. Block, W. Fan, and J. M. DiMaio, “Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device,” in SPIE Sensing Technology+ Applications (International Society for Optics and Photonics, 2014), pp. 910718–910718.

Ponticorvo, A.

D. M. Burmeister, A. Ponticorvo, B. Yang, S. C. Becerra, B. Choi, A. J. Durkin, and R. J. Christy, “Utility of spatial frequency domain imaging (sfdi) and laser speckle imaging (lsi) to non-invasively diagnose burn depth in a porcine model,” Burns 41, 1242–1252 (2015).
[Crossref] [PubMed]

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

Ragol, S.

S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
[Crossref]

Remer, I.

S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
[Crossref]

Riola, K.

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

Rodriguez-Vaqueiro, Y.

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

Rosenberg, L.

S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
[Crossref]

R. Gurfinkel, L. Rosenberg, S. Cohen, A. Cohen, A. Barezovsky, E. Cagnano, and A. J. Singer, “Histological assessment of tangentially excised burn eschars,” The Canadian Journal of Plastic Surgery 18, e33 (2010).
[Crossref]

Sadasivam, M.

P. Avci, A. Gupta, M. Sadasivam, D. Vecchio, Z. Pam, N. Pam, and M. R. Hamblin, “Low-level laser (light) therapy (lllt) in skin: stimulating, healing, restoring,” in Seminars in Cutaneous Medicine and Surgery, vol. 32 (Frontline Medical Communications, 2013), pp. 41–52.

Saleh, B.

B. Saleh, Introduction to Subsurface Imaging (Cambridge University Press, 2011).
[Crossref]

Sellke, E.

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

Sellke, E. W.

J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
[Crossref] [PubMed]

W. Li, W. Mo, X. Zhang, J. J. Squiers, Y. Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” J. Biomed. Opt. 20, 121305 (2015).
[Crossref] [PubMed]

Shoham, Y.

S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
[Crossref]

Sinelnikov, I.

S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
[Crossref]

Singer, A. J.

R. Gurfinkel, L. Rosenberg, S. Cohen, A. Cohen, A. Barezovsky, E. Cagnano, and A. J. Singer, “Histological assessment of tangentially excised burn eschars,” The Canadian Journal of Plastic Surgery 18, e33 (2010).
[Crossref]

Spence, R. J.

L. Devgan, S. Bhat, S. Aylward, and R. J. Spence, “Modalities for the assessment of burn wound depth,” Journal of Burns and Wounds 5, e2 (2006).
[PubMed]

Squiers, J. J.

J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
[Crossref] [PubMed]

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

W. Li, W. Mo, X. Zhang, J. J. Squiers, Y. Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” J. Biomed. Opt. 20, 121305 (2015).
[Crossref] [PubMed]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

J. Heredia-Juesas, J. E. Thatcher, Y. Lu, J. J. Squiers, D. R. King, W. Fan, J. M. DiMaio, and J. A. Martinez-Lorenzo, “Non-invasive optical imaging techniques for burn-injured tissue detection for debridement surgery,”in International Conference of the IEEE Engineering in Medicine and Biology Society (2016).

Thatcher, J. E.

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
[Crossref] [PubMed]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

W. Li, W. Mo, X. Zhang, J. J. Squiers, Y. Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” J. Biomed. Opt. 20, 121305 (2015).
[Crossref] [PubMed]

J. E. Thatcher, K. D. Plant, D. R. King, K. L. Block, W. Fan, and J. M. DiMaio, “Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device,” in SPIE Sensing Technology+ Applications (International Society for Optics and Photonics, 2014), pp. 910718–910718.

J. Heredia-Juesas, J. E. Thatcher, Y. Lu, J. J. Squiers, D. R. King, W. Fan, J. M. DiMaio, and J. A. Martinez-Lorenzo, “Non-invasive optical imaging techniques for burn-injured tissue detection for debridement surgery,”in International Conference of the IEEE Engineering in Medicine and Biology Society (2016).

Tibshirani, R.

J. Friedman, T. Hastie, and R. Tibshirani, The Elements of Statistical Learning, vol. 1, Springer Series in Statistics (Springer2001).

Tiede, D.

T. Blaschke, D. Tiede, and S. Lang, “An object-based information extraction methodology incorporating a-priori spatial information,” in ESA Conference on Image Information Mining (2006).

Uchitel, D.

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

Vecchio, D.

P. Avci, A. Gupta, M. Sadasivam, D. Vecchio, Z. Pam, N. Pam, and M. R. Hamblin, “Low-level laser (light) therapy (lllt) in skin: stimulating, healing, restoring,” in Seminars in Cutaneous Medicine and Surgery, vol. 32 (Frontline Medical Communications, 2013), pp. 41–52.

Wang, Y.

J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
[Crossref] [PubMed]

Willenz, U.

S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
[Crossref]

Yang, B.

D. M. Burmeister, A. Ponticorvo, B. Yang, S. C. Becerra, B. Choi, A. J. Durkin, and R. J. Christy, “Utility of spatial frequency domain imaging (sfdi) and laser speckle imaging (lsi) to non-invasively diagnose burn depth in a porcine model,” Burns 41, 1242–1252 (2015).
[Crossref] [PubMed]

Zhang, X.

W. Li, W. Mo, X. Zhang, J. J. Squiers, Y. Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” J. Biomed. Opt. 20, 121305 (2015).
[Crossref] [PubMed]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

Advances in Wound Care (1)

J. E. Thatcher, J. J. Squiers, S. C. Kanick, D. R. King, Y. Lu, Y. Wang, R. Mohan, E. W. Sellke, and J. M. DiMaio, “Imaging techniques for clinical burn assessment with a focus on multispectral imaging,” Advances in Wound Care 5, 360–378 (2016).
[Crossref] [PubMed]

Burns (2)

D. M. Burmeister, A. Ponticorvo, B. Yang, S. C. Becerra, B. Choi, A. J. Durkin, and R. J. Christy, “Utility of spatial frequency domain imaging (sfdi) and laser speckle imaging (lsi) to non-invasively diagnose burn depth in a porcine model,” Burns 41, 1242–1252 (2015).
[Crossref] [PubMed]

D. R. King, W. Li, J. J. Squiers, R. Mohan, E. Sellke, W. Mo, X. Zhang, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging,” Burns 41, 1478–1487 (2015).
[Crossref] [PubMed]

IEEE Transactions on Geoscience Electronics (1)

R. L. Kettig and D. Landgrebe, “Classification of multispectral image data by extraction and classification of homogeneous objects,” IEEE Transactions on Geoscience Electronics 14, 19–26 (1976).
[Crossref]

J. Biomed. Opt. (3)

W. Li, W. Mo, X. Zhang, J. J. Squiers, Y. Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” J. Biomed. Opt. 20, 121305 (2015).
[Crossref] [PubMed]

J. Q. Nguyen, C. Crouzet, T. Mai, K. Riola, D. Uchitel, L.-H. Liaw, N. Bernal, A. Ponticorvo, B. Choi, and A. J. Durkin, “Spatial frequency domain imaging of burn wounds in a preclinical model of graded burn severity,” J. Biomed. Opt. 18, 066010 (2013).
[Crossref]

S. Ragol, I. Remer, Y. Shoham, S. Hazan, U. Willenz, I. Sinelnikov, V. Dronov, L. Rosenberg, and A. Bilenca, “Static laser speckle contrast analysis for noninvasive burn diagnosis using a camera-phone imager,” J. Biomed. Opt. 20, 086009 (2015).
[Crossref]

Journal of Burn Care & Research (1)

J. E. Thatcher, W. Li, Y. Rodriguez-Vaqueiro, J. J. Squiers, W. Mo, Y. Lu, K. D. Plant, E. Sellke, D. R. King, W. Fan, and et al.,“Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision,” Journal of Burn Care & Research 37, 38–52 (2016).
[Crossref]

Journal of Burns and Wounds (1)

L. Devgan, S. Bhat, S. Aylward, and R. J. Spence, “Modalities for the assessment of burn wound depth,” Journal of Burns and Wounds 5, e2 (2006).
[PubMed]

New England Journal of Medicine (1)

D. P. Orgill, “Excision and skin grafting of thermal burns,” New England Journal of Medicine 360, 893–901 (2009).
[Crossref] [PubMed]

The Canadian Journal of Plastic Surgery (1)

R. Gurfinkel, L. Rosenberg, S. Cohen, A. Cohen, A. Barezovsky, E. Cagnano, and A. J. Singer, “Histological assessment of tangentially excised burn eschars,” The Canadian Journal of Plastic Surgery 18, e33 (2010).
[Crossref]

Other (7)

J. Heredia-Juesas, J. E. Thatcher, Y. Lu, J. J. Squiers, D. R. King, W. Fan, J. M. DiMaio, and J. A. Martinez-Lorenzo, “Non-invasive optical imaging techniques for burn-injured tissue detection for debridement surgery,”in International Conference of the IEEE Engineering in Medicine and Biology Society (2016).

J. Friedman, T. Hastie, and R. Tibshirani, The Elements of Statistical Learning, vol. 1, Springer Series in Statistics (Springer2001).

T. Blaschke, D. Tiede, and S. Lang, “An object-based information extraction methodology incorporating a-priori spatial information,” in ESA Conference on Image Information Mining (2006).

J. E. Thatcher, K. D. Plant, D. R. King, K. L. Block, W. Fan, and J. M. DiMaio, “Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device,” in SPIE Sensing Technology+ Applications (International Society for Optics and Photonics, 2014), pp. 910718–910718.

B. Saleh, Introduction to Subsurface Imaging (Cambridge University Press, 2011).
[Crossref]

P. Avci, A. Gupta, M. Sadasivam, D. Vecchio, Z. Pam, N. Pam, and M. R. Hamblin, “Low-level laser (light) therapy (lllt) in skin: stimulating, healing, restoring,” in Seminars in Cutaneous Medicine and Surgery, vol. 32 (Frontline Medical Communications, 2013), pp. 41–52.

D. Barolet, “Light-emitting diodes (leds) in dermatology,” in Seminars in cutaneous medicine and surgery, vol. 27 (Frontline Medical Communications, 2008), pp. 227–238.
[Crossref]

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Figures (12)

Fig. 1
Fig. 1 Three independent optical imaging techniques collects the information of the subject: (a) temporal variation, (b) Spatial texture of the image, and (c) Multispectral reflection. The features if these techniques are combined in order to improve the accuracy of the tissue classification.
Fig. 2
Fig. 2 Optical data system: (a) Monochromatic light scattered from tissue. When measured over temporal interval, the intensity changes in the back-scattered light produce a PPG waveform; (b) Multispectral imager measures 8 wavelengths of light. This system quickly collects an image at each position to generate a spectral data cube [8].
Fig. 3
Fig. 3 Block diagram of the PPG Output preprocessing. In italics, the metrics obtained during this process.
Fig. 4
Fig. 4 Frequency representation (in terms of heart rates) of two PPG signals: (a) from a pixel with good HR information, (b) from a noisy pixel. The metrics of the first four features are calculated from the parameters described in the plot.
Fig. 5
Fig. 5 Example of the RGB image and the six initial features of one injury.
Fig. 6
Fig. 6 Location of the injuries on the back of the pig.
Fig. 7
Fig. 7 The left side represents a partial thickness burn showing the histologists markings on the tissue. The right side shows the penetration in the skin for different wavelengths of light.
Fig. 8
Fig. 8 (a) Real image and (b) Ground Truth of the post-injury case 3A1. (c) Real image and (d) Ground Truth of the first excision case 1B1.
Fig. 9
Fig. 9 (a)–(b) Regular classification and (c)–(d) classification after applying the mode filtering post-processing, for the post-injury step of the case 3A1. Each row plots the results using the features of the indicated imaging technique. (a) and (c) columns plot the classification image, and (b) and (d) columns plot the classification error (blue color indicates correct classification and yellow indicates wrong classification), indicating, as well, the total error of classification.
Fig. 10
Fig. 10 (a)–(b) Regular classification and (c)–(d) classification after applying the mode filtering post-processing, for the first cut step of the case 1B1. Each row plots the results using the features of the indicated imaging technique. (a) and (c) columns plot the classification image, and (b) and (d) columns plot the classification error (blue color indicates correct classification and yellow indicates wrong classification), indicating, as well, the total error of classification.
Fig. 11
Fig. 11 Graphical example of the application of the mode filtering post-processing. The pixel in the middle of the image is reclassified as the most repeated value of the 11 × 11 box.
Fig. 12
Fig. 12 Accuracy per class and global accuracy of the performance, based on the set of features used for classification. The lighter colors indicate the improvement after applying the mode filtering post-processing. Error boxes indicate ±1 standard deviation from the mean values.

Tables (8)

Tables Icon

Table 1 Wavelengths of the MSI.

Tables Icon

Table 2 Description of all the features used in the experiment

Tables Icon

Table 3 Penetration in skin for different wavelengths

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Table 4 Number of features used for each imaging technique

Tables Icon

Table 5 Example of a confusion matrix

Tables Icon

Table 6 Accuracy mean comparison when combining different imaging techniques

Tables Icon

Table 7 Comparison of the accuracy mean when combining different imaging techniques, plus the use of the mode filtering post-processing

Tables Icon

Table 8 Accuracy improvement after applying the mode filtering post-processing, relative to the regular classification

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

G ^ ( x ) = arg max k P ( G = k | X = x ) = arg max k f k ( x ) π k = arg max k [ δ k ( x ) ] ,
δ k ( x ) = 1 2 log | Σ k | 1 2 ( x μ k ) T Σ k 1 ( x μ k ) + log π k .
A i = R i r i ,
A = 1 N C i = 1 N C A i ,

Metrics