Textural patterns are often complex, exhibit scale-dependent changes in structure and are difficult to identify and describe. The lacunarity morphometric uses multiscale windowing to measure the scale dependency of spatial heterogeneity. In the current work, the objectives are to investigate the usefulness of lacunarity, using different colour scales (R, G, B, L*, a*, b*, and Gray), as a quantitative descriptor of visual texture in sliced ham surfaces, and compare these results with the binary approach developed by Valous et al. (2009b). Images were acquired from three qualities of sliced pork ham, typically consumed in Ireland (200 slices/quality). Lacunarity plots reveal important textural content information that corresponds to degree of spatial heterogeneity of intensities and level of self-similar behaviour. The results of intensity lacunarity suggest that window sizes up to 10 pixels may be adequate enough to cover textural features and produce meaningful results. Once the box size is larger than 10, lacunarity curves either converge or display atypical behaviour and then decay. Investigation confirmed that both binary and intensity lacunarity approaches are useful quantitative descriptors of visual texture in sliced ham images. Moreover, potential future research directions were suggested for computing lacunarity in colour images directly. © 2009 Elsevier Ltd. All rights reserved.