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In dermatology, the optical coherence tomography (OCT) is used to visualize the skin over few millimeters depth. These images are affected by speckle, which can alter their interpretation, but which also carries information that characterizes locally the visualized tissue. In this paper, we propose to differentiate the skin layers by modeling locally the speckle in OCT images. The performances of four probability density functions (Rayleigh, Lognormal, Nakagami and Generalized Gamma) to model the distribution of speckle in each skin layer are analyzed. From this study, we propose to classify the pixels of OCT images using the estimated parameters of the most appropriate distribution. Quantitative results with 30 images are compared to the manual delineations of five experts. The results confirm the potential of the method to generate useful data for robust segmentation.