An important goal for characterization of texture appearance is the quantification of spatial patterns. The objective was to investigate the potential usefulness of fractal lacunarity as a descriptor of visual texture in ham slice images. Images were acquired from three qualities of pork ham slices typically consumed in Ireland (200 slices/quality). The segmented images of pores/defects and fatconnective tissue were separately evaluated to compute lacunarity curves. Lacunarity is smaller when the binary image is nearly translationally invariant, being made of "diffuse" clumps separated by smaller empty lacunas, like in the case of the medium quality slices. Lacunarity is higher when the image (premium quality slices) consists of structures in the form of "tight" clumps separated by larger empty gaps. Curves appear representative for the characterization of texture appearance, due to the changes of lacunarity over different gliding box sizes. Additionally, normalized lacunarity plots emphasize more, all the typical features which are already found in lacunarity curves. The behaviour of lacunarity curves resembles the one of an exponential decay and the fitting of these curves was carried out using a three-parameter (m, n, and q) single exponential decay function. Results suggest that lacunarity has a discriminating effect among the three ham qualities which is more apparent in the pores/defects binary images, where the curves differentiate the ham qualities better than in the fat-connective tissue images. This investigation confirmed the usefulness of this fractal metric as a quantitative descriptor of texture appearance in sliced ham images.