Abstract

Optical coherence tomography (OCT) provides a non-invasive method for in-vivo imaging of sub-surface skin tissue. Many skin features such as sweat glands and blisters are clearly observable in OCT images. It seems therefore probable that OCT could be used for the detection and identification of lesions and skin cancers. These applications, however, have not been well developed. One area in dermatology where OCT has been applied is the measurement of epidermal thickness. OCT images are inherently noisy and measurements based on them require intensive manual processing. A robust method to automatically detect and measure features of interest is necessary to enable routine application of OCT. As a first step, we approach the seemingly straightforward problem of measuring epidermal thickness. In this paper we describe a novel shapelet-based image processing technique for the automatic identification of the upper and lower boundaries of the epidermis in living human skin tissue. These boundaries are used to measure epidermal thickness. To our knowledge, this is the first report of automated feature identification and measurement from OCT images of skin.

© 2004 Optical Society of America

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References

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  1. M. Rajajadhyaksha, S. Gonzalez, J. M. Zavislan, R. R. Anderson, R. H. Webb, �??In vivo confocal scanning laser microscopy of human skin II: Advances in instrumentation and comparison with histology,�?? J. Invest. Dermatol. 113 (1999) 293
    [CrossRef]
  2. P. J. Caspers, G. W. Lucassen, G. J. Puppels, �??Combined In Vivo Confocal Raman Spectroscopy and Confocal Microscopy of Human Skin,�?? Biophy J., 85 (1), 572-580 (2003)
    [CrossRef]
  3. B. R. Masters, P. T. C. So, and E. Gratton, "Multiphoton excitation fluorescence miscroscopy and spectroscopy of in vivo human skin," Biophy. J. 72, 2405-2412 (1997).
    [CrossRef]
  4. N. D. Gladkova, G. A. Petrova, N. K. Nikulin, S. G. Radenska-Lopovok, L. B. Snopova, Y. P. Chumakov, V. A. Nasonova, V. M. Gelikonov, G. V. Gelikonov, R. V. Kuranov, A. M. Sergeev, F. I. Feldchtein, �??In vivo optical coherence tomography imaging of human skin: norm and pathology,�?? Skin Research & Technol. 6, 6-16 (2000)
    [CrossRef]
  5. J. Welzel, �??Optical coherence tomography in dermatology: a review,�?? Skin Research & Tech. 7, 1-9 (2001)
    [CrossRef]
  6. A. Knuttel and M. Boehlau-Godau, �??Spatially confined and temporally resolved refractive index and scattering evaluation in human skin performed with optical coherence tomography," J. Biomed. Opt. 5, 83- 92 (2000)
    [CrossRef] [PubMed]
  7. J.M. Schmidt, S.H. Xiang, and K.M. Yung, �??Speckle in optical coherence tomography,�?? J. Biomedical Optics 4, 95-105 (1999).
    [CrossRef]
  8. L.A. Goldsmith (ed.), Physiology, Biochemistry, and Molecular Biology of Skin. Oxford University Press, New York, (1991)
  9. S. Neerken, G.W. Lucassen, M. A. Bisschop, E. Lenderink, and T.A.M. Nuijs.,�?? Characterization of agerelated effects in human skin: A comparative study that applies confocal laser scanning microscopy and optical coherence tomography ,�?? J. Biomed. Opt. 9(2) 274-281 (2004)
    [CrossRef]
  10. F. G. Bechara, T. Gambichler, M. Stucker, A. Orklikov, S. Rotterdam, P. Altmeyer and K. Hoffmann, �??Histomomorphologic correlation with routine histology and optical coherence tomography.�?? Skin Research & Technol. 10, 169-173 (2004)
    [CrossRef]
  11. E. R. Malinowski, Factor Analysis in Chemistry, 2nd ed., Wiley, New York, (1991).
  12. A. Refregier, �??Shapelets �?? I. A method for image analysis,�?? Mon. Not. R. Astron. Soc. 338, 35-47 (2003).
    [CrossRef]
  13. P. Kovesi, Proc. Australia-Japan Advanced Workshop on Computer Vision, 9-11 September 2003, Adelaide. p. 101-108.
  14. P. J. Rousseeuw, �??Least median of squares regression,�?? J. Amer. Statistical Assoc., 79, 871-880 (1984)
    [CrossRef]
  15. M. C. Pierce, J. Strasswimmer, B. H. Park, B. Cense, and J. F. deBoer, �??Advances in Optical Coherence Tomography Imaging for Dermatology�?? J. Biomed. Opt. 9 (2), 287-291 (2004)
    [CrossRef] [PubMed]

Biophy J. (1)

P. J. Caspers, G. W. Lucassen, G. J. Puppels, �??Combined In Vivo Confocal Raman Spectroscopy and Confocal Microscopy of Human Skin,�?? Biophy J., 85 (1), 572-580 (2003)
[CrossRef]

Biophy. J. (1)

B. R. Masters, P. T. C. So, and E. Gratton, "Multiphoton excitation fluorescence miscroscopy and spectroscopy of in vivo human skin," Biophy. J. 72, 2405-2412 (1997).
[CrossRef]

J. Amer. Statistical Assoc. (1)

P. J. Rousseeuw, �??Least median of squares regression,�?? J. Amer. Statistical Assoc., 79, 871-880 (1984)
[CrossRef]

J. Biomed. Opt. (3)

M. C. Pierce, J. Strasswimmer, B. H. Park, B. Cense, and J. F. deBoer, �??Advances in Optical Coherence Tomography Imaging for Dermatology�?? J. Biomed. Opt. 9 (2), 287-291 (2004)
[CrossRef] [PubMed]

A. Knuttel and M. Boehlau-Godau, �??Spatially confined and temporally resolved refractive index and scattering evaluation in human skin performed with optical coherence tomography," J. Biomed. Opt. 5, 83- 92 (2000)
[CrossRef] [PubMed]

S. Neerken, G.W. Lucassen, M. A. Bisschop, E. Lenderink, and T.A.M. Nuijs.,�?? Characterization of agerelated effects in human skin: A comparative study that applies confocal laser scanning microscopy and optical coherence tomography ,�?? J. Biomed. Opt. 9(2) 274-281 (2004)
[CrossRef]

J. Biomedical Optics (1)

J.M. Schmidt, S.H. Xiang, and K.M. Yung, �??Speckle in optical coherence tomography,�?? J. Biomedical Optics 4, 95-105 (1999).
[CrossRef]

J. Invest. Dermatol. (1)

M. Rajajadhyaksha, S. Gonzalez, J. M. Zavislan, R. R. Anderson, R. H. Webb, �??In vivo confocal scanning laser microscopy of human skin II: Advances in instrumentation and comparison with histology,�?? J. Invest. Dermatol. 113 (1999) 293
[CrossRef]

Mon. Not. R. Astron. Soc. (1)

A. Refregier, �??Shapelets �?? I. A method for image analysis,�?? Mon. Not. R. Astron. Soc. 338, 35-47 (2003).
[CrossRef]

Skin Research & Tech. (1)

J. Welzel, �??Optical coherence tomography in dermatology: a review,�?? Skin Research & Tech. 7, 1-9 (2001)
[CrossRef]

Skin Research & Technol. (2)

N. D. Gladkova, G. A. Petrova, N. K. Nikulin, S. G. Radenska-Lopovok, L. B. Snopova, Y. P. Chumakov, V. A. Nasonova, V. M. Gelikonov, G. V. Gelikonov, R. V. Kuranov, A. M. Sergeev, F. I. Feldchtein, �??In vivo optical coherence tomography imaging of human skin: norm and pathology,�?? Skin Research & Technol. 6, 6-16 (2000)
[CrossRef]

F. G. Bechara, T. Gambichler, M. Stucker, A. Orklikov, S. Rotterdam, P. Altmeyer and K. Hoffmann, �??Histomomorphologic correlation with routine histology and optical coherence tomography.�?? Skin Research & Technol. 10, 169-173 (2004)
[CrossRef]

Other (3)

E. R. Malinowski, Factor Analysis in Chemistry, 2nd ed., Wiley, New York, (1991).

L.A. Goldsmith (ed.), Physiology, Biochemistry, and Molecular Biology of Skin. Oxford University Press, New York, (1991)

P. Kovesi, Proc. Australia-Japan Advanced Workshop on Computer Vision, 9-11 September 2003, Adelaide. p. 101-108.

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Figures (6)

Fig. 1.
Fig. 1.

OCT images of skin. (a) Volar forearm, showing skin layers. b) Thumb, with thick stratum corneum and visible sweat glands.

Fig. 2.
Fig. 2.

A-Scan analysis of an OCT image of skin.

Fig. 3.
Fig. 3.

Shapelet decomposition of OCT skin images. a) Raw image. b) Short lengthscale shapelet highlights stratum corneum. c) Mid and long lengthscale shapelets find DEJ. d) Reconstructed image with shapelet results.

Fig. 4.
Fig. 4.

Comparison of DEJ location as determined by trained operators (cyan, red, and magenta lines) and the shapelet analysis program (yellow line). The stratum corneum position is shown by the top green line, as determined by shapelet analysis.

Fig. 5.
Fig. 5.

Identification of the DEJ in a non-ideal OCT image. The software correctly traces the DEJ even with a curved surface and bubbles in the ultrasound gel.

Fig. 6.
Fig. 6.

Results of principal components (PC) analysis on epidermal thickness measurements for A-scan, shapelet analysis, and manual line drawing. Clustering of manual and shapelet results indicate good agreement in the majority of cases.

Tables (1)

Tables Icon

Table 1. Comparison of epidermal thickness measured from 47 OCT images using A-scan, shapelet analysis, and manual line-drawing by three different people. Shapelet analysis performs similarly to human operators, and has a much lower error rate as compared to the A-scans.

Equations (6)

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τ = arctan 2 ( G y , G x )
G = ( G x 2 + G y 2 ) 1 2
σ = arctan ( [ G ] )
C i = G * si
C τ i = cos ( τ G ) * cos ( τ si ) + sin ( τ G ) * ( τ si )
C i = C i . C τ i

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