The choice of the most useful features to extract from digital images depends on the results of epiluminescence pattern analysis. Although the system saves the microscope magnifications along with the texture analysis, offering an error-free objective evaluation, the different magnifications could confuse clinicians wanting to make subjective comparisons of lesions. In this article we only discuss the ×16 images. The system uses an algorithm based on the Laplacian-of-Gaussian for the segmentation procedure and a zero-crossing algorithm for the border automatic outline.35 Once the borders had been automatically detected and traced, the system evaluated 36 variables to be studied as possible discriminant variables. The reproducibility was first tested on digitalized images of 100 lesions belonging to 20 subjects (1 PSL for each patient recorded 5 times at regular 15-minute intervals). Absolute differences between single measurements and mean values of a given lesion or variable never exceeded 3% of the mean value. The studied variables belonged to 4 categories: geometries, colors, textures, and islands of color (ie, color clusters inside the lesion). Geometries includes area, perimeter, maximum and minimum diameters, variance of contour symmetry, circularity, and fractality of borders. Colors includes red, green, and blue lesion mean values; red, green, and blue healthy skin mean values; red, green, and blue lesion deciles; red, green and blue lesion quartiles, and skin-lesion gradient mean value. Texture includes mean contrast and entropy of the lesion and contrast and entropy fractality. Islands of color includes peripheral dark regions, dark area, dark regions imbalance, imbalance, green area, green dominant regions imbalance, blue-gray area, blue-gray regions, transition area, transition regions imbalance. The meanings of all variables are explained in Table 1, Table 2, Table 3, and Table 4.