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Keratinocytes in skin epidermis, which have bright cytoplasmic contrast and dark nuclear contrast in reflectance confocal microscopy (RCM), were modeled with a simple error function reflectance profile: erf( ). Forty-two example keratinocytes were identified as a training set which characterized the nuclear size a = 8.6±2.8 μm and reflectance gradient b = 3.6±2.1 μm at the nuclear∕cytoplasmic boundary. These mean a and b parameters were used to create a rotationally symmetric erf( ) mask that approximated the mean keratinocyte image. A computer vision algorithm used an erf( ) mask to scan RCM images, identifying the coordinates of keratinocytes. Applying the mask to the confocal data identified the positions of keratinocytes in the epidermis. This simple model may be used to noninvasively evaluate keratinocyte populations as a quantitative morphometric diagnostic in skin cancer detection and evaluation of dermatological cosmetics.

Automated identification of epidermal keratinocytes in reflectance confocal microscopy