  This paper deals with the characterization and classification of reflectance
confocal microscopy images of human skin. The aim is to identify and
characterize the lentigo, a phenomenon that originates at the dermo-epidermic
junction of the skin. High resolution confocal images are acquired at different
skin depths and are analyzed for each depth. Histograms of pixel intensities
associated with a given depth are determined, showing that the generalized
gamma distribution (GGD) is a good statistical model for confocal images. A GGD
is parameterized by translation, scale and shape parameters. These parameters
are estimated using a new estimation method based on a natural gradient descent
showing fast convergence properties with respect to state-of-the-art estimation
methods. The resulting parameter estimates can be used to classify clinical
images of healthy and lentigo patients. The obtained results show that the
scale and shape parameters are good features to identify and characterize the
presence of lentigo in skin tissues.
