The elusive dark matter is the best candidate in order to explain gravitational effects such as, for example, the motion of stars in galaxies. We introduce a novel method for the measurement of information in cosmic images called maximal nucleus clustering (MNC) i.e., nucleus clustering’s Rényi entropy derived from strong proximities in feature-based Voronoï tessellations. MNC is a novel, fast and inexpensive image-analysis technique, independent from other detectable signals. It permits the assessment of changes in gradient orientation into zones of two-dimensional cosmic images that generally are not taken into account by other techniques. In order to evaluate the potential applications of MNC, we looked for the presence of MNC’s distinctive hallmarks in the plane surface of astronomic images. We found that Rényi entropy is higher in MNC areas of cosmic images than in the surrounding regions, and that these patterns are correlated with cosmic zones containing a lesser amount of dark energy. Therefore, computational geometry provides a bridge made of affine connexions and proximities between features of a two-dimensional pictures and physical features of the Universe.