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Optical coherence tomography of skin for measurement of epidermal thickness by shapelet-based image analysis

Jesse Weissman, Tom Hancewicz, and Peter Kaplan

Open Access Open Access

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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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.

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Fig. 2.
Fig. 2. A-Scan analysis of an OCT image of skin.

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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.

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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.

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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.

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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.

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Tables (1)

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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.

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Equations (6)

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

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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