  This paper studies a new Bayesian algorithm for the joint reconstruction and
classification of reflectance confocal microscopy (RCM) images, with
application to the identification of human skin lentigo. The proposed Bayesian
approach takes advantage of the distribution of the multiplicative speckle
noise affecting the true reflectivity of these images and of appropriate priors
for the unknown model parameters. A Markov chain Monte Carlo (MCMC) algorithm
is proposed to jointly estimate the model parameters and the image of true
reflectivity while classifying images according to the distribution of their
reflectivity. Precisely, a Metropolis-whitin-Gibbs sampler is investigated to
sample the posterior distribution of the Bayesian model associated with RCM
images and to build estimators of its parameters, including labels indicating
the class of each RCM image. The resulting algorithm is applied to synthetic
data and to real images from a clinical study containing healthy and lentigo
patients.
