Detection of the dermis/epidermis boundary in reflectance confocal images using multi-scale classifier with adaptive texture features

Reflectance confocal microscopy is an emerging modality for dermatology applications, especially in-situ and bedside detection of skin cancers. Work to date has concentrated on hardware development and validation by clinicians in comparison with standard histological staining. As this technology gains acceptance, the development of automated processing methods becomes more important. We concentrate here on detection of the dominant internal feature of the skin, the epidermis/dermis boundary, a complex corrugated 3-dimensional layer marked by optically subtle changes and features. We adopt a machine learning approach to this segmentation problem, using a hierarchical multi-scale classifier with sophisticated on-line feature selection, to minimize the required expert labeling and maximize the range of potential features in the face of high inter- and intra-subject variability and low optical contrast. Initial results indicate the ability of our approach to recover the complex 3-D boundary surface.