Automatic Localization of Skin Layers in Reflectance Confocal Microscopy

Abstract

Reflectance Confocal Microscopy (RCM) is a noninvasive imaging tool used in clinical dermatology and skin research, allowing real time visualization of skin structural features at different depths at a resolution comparable to that of conventional histology [1]. Currently, RCM is used to generate a rich skin image stack (about 60 to 100 images per scan) which is visually inspected by experts, a process that is tedious, time consuming and exclusively qualitative. Based on the observation that each of the skin images in the stack can be characterized as a texture, we propose a quantitative approach for automatically classifying the images in the RCM stack, as belonging to the different skin layers: stratum corneum, stratum granulosum, stratum spinosum, stratum basale, and the papillary dermis. A reduced set of images in the stack are used to generate a library of representative texture features named textons. This library is employed to characterize all the images in the stack with a corresponding texton histogram. The stack is ultimately separated into 5 different sets of images, each corresponding to different skin layers, exhibiting good correlation with expert grading. The performance of the method is tested against three RCM stacks and we generate promising classification results. The proposed method is especially valuable considering the currently scarce landscape of quantitative solutions for RCM imaging.