Entropy analysis of images are usually performed using Shannon entropy, which calculates the probability of occurrency of each gray level on the image. However, not only the pixel gray level but also the spatial distribution of pixels might be important for image analysis. On the other hand, sample entropy (SampEn) is an important tool for estimation of irregularity in time series, which calculates the probability of pattern occurrence within the series. Therefore, we propose here an extension of SampEn to a two-dimensional case, namely SampEn2D, as an entropy method for extracting features from images that accounts for the spatial distribution of pixels. SampEn2D was applied to histological segments of sural nerve obtained from young (30 days) and elderly (720 days) rats. Morphometric indexes, such as the total number of myelinated fibers and the average myelinated fibers area and perimeter were also calculated. Results show that SampEn2D can extract useful information from histological nerve images, classifying elderly rat image as more regular than young rat. As SampEn2D is related to irregularity/unpredictability, we can conclude that the proposed method is complementary to morphometric indexes. Further studies are being built to validate SampEn2D.