This overview is designed to illuminate the concept and utility of lacunarity. We first establish a strong foundation with a pedagogical introduction to the lacunarity measure applied to images, detailing analytical examples and a general approach. In the second part, we compare the available software for estimating the lacunarity of images. Related to this goal, we also provide an open-source code in R and Python. The third part then synthesizes these theoretical and computational aspects by presenting an analysis of the diverse applications of lacunarity across various scientific disciplines, utilizing VOSviewer networks to visually organize research topics into distinct clusters. We identify distinct thematic clusters in materials science, biological systems, and medical imaging.
Pinto et al. (Fri,) studied this question.
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