Semi-automated Algorithm for Localization of Dermal/ Epidermal Junction in Reflectance Confocal Microscopy Images of Human Skin

Abstract

The examination of the dermis/epidermis junction (DEJ) is clinically important for skin cancer diagnosis. Reflectance confocal microscopy (RCM) is an emerging tool for detection of skin cancers in vivo. However, visual localization of the DEJ in RCM images, with high accuracy and repeatability, is challenging, especially in fair skin, due to low contrast, heterogeneous structure and high inter- and intra-subject variability. We recently proposed a semi-automated algorithm to localize the DEJ in z-stacks of RCM images of fair skin, based on feature segmentation and classification. Here we extend the algorithm to dark skin. The extended algorithm first decides the skin type and then applies the appropriate DEJ localization method. In dark skin, strong backscatter from the pigment melanin causes the basal cells above the DEJ to appear with high contrast. To locate those high contrast regions, the algorithm operates on small tiles (regions) and finds the peaks of the smoothed average intensity depth profile of each tile. However, for some tiles, due to heterogeneity, multiple peaks in the depth profile exist and the strongest peak might not be the basal layer peak. To select the correct peak, basal cells are represented with a vector of texture features. The peak with most similar features to this feature vector is selected. The results show that the algorithm detected the skin types correctly for all 17 stacks tested (8 fair, 9 dark). The DEJ detection algorithm achieved an average distance from the ground truth DEJ surface of around 4.7μm for dark skin and around 7-14μm for fair skin.

Figure 1

The left panel shows the structure of skin in a vertical cross section diagram. The DEJ is the thin membrane, marked in red, that separates the epidermis from the dermis. The basal layer lies directly on the DEJ. The basal layer is typically at average depth of 100 μm below the surface in normal skin and 10–16 μm in thickness [1]. The right figure shows a vertical slice from an RCM volume constructed from an RCM image stack of dark skin. The layer of bright regions corresponds to the basal layer including more melanin with high reflectivity.

Figure 2

The left and right panels show two slices from an RCM stack from fair skin (on the left) and dark skin (on the right) respectively. The white boundary drawn is the DEJ.

Figure 3

Flow chart for the automated skin type detection algorithm

Figure 4

The figure shows a sample mean intensity profile of a tile along depth direction (in μm). The peak detection algorithm found 3 peaks shown with blue dots and red star. The peak selection algorithm selected the right peak (red star) corresponding to the basal layer. Next, the lower boundary of the basal layer (i.e. the DEJ) was found as the closest inflection point of the mean intensity profile function to this selected peak (shown with magenta plus sign).

Figure 5

The upper and lower panels on the right compare the DEJ found by the algorithm (dotted red) with the one marked by the expert (green) for two sample vertical cross sections (x–z) and (y–z) from the RCM stack 1. The solid lines in the left figures indicate the vertical slice location on a sample horizontal slice. Note that the expert marks the DEJ not on the vertical slices but on horizontal slices.

Figure 6

The same figure as Figure 5 from a different RCM stack 2.

Figure 7

Surface plot of the DEJ automatically found by the algorithm is shown in 3D in comparison to expert labeled DEJ for RCM stack 1 (data1r2). The surface itself indicates the resultant DEJ of the algorithm and the color map indicates the distance from the expert labeled DEJ (error).

Figure 8

The same figure as Fig. 6 for RCM stack 2(data3r3).