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This paper studies a new Bayesian algorithm for the joint reconstruction andclassification of reflectance confocal microscopy (RCM) images, withapplication to the identification of human skin lentigo. The proposed Bayesianapproach takes advantage of the distribution of the multiplicative specklenoise affecting the true reflectivity of these images and of appropriate priorsfor the unknown model parameters. A Markov chain Monte Carlo (MCMC) algorithmis proposed to jointly estimate the model parameters and the image of truereflectivity while classifying images according to the distribution of theirreflectivity. Precisely, a Metropolis-whitin-Gibbs sampler is investigated tosample the posterior distribution of the Bayesian model associated with RCMimages and to build estimators of its parameters, including labels indicatingthe class of each RCM image. The resulting algorithm is applied to syntheticdata and to real images from a clinical study containing healthy and lentigopatients.

An unsupervised bayesian approach for the joint reconstruction and classification of cutaneous reflectance confocal microscopy images