  We pursue a novel morphometric analysis to detect sources in very-high-energy
gamma-ray counts maps by structural deviations from the background noise
without assuming any prior knowledge about potential sources. The rich and
complex structure of the background noise is characterized by Minkowski
functionals from integral geometry. By extracting more information out of the
same data, we aim for an increased sensitivity. In the first two papers, we
derived accurate estimates of the joint distribution of all Minkowski
functionals. Here, we use this detailed structure characterization to detect
structural deviations from the background noise in a null hypothesis test. We
compare the analysis of the same simulated data with either a single or all
Minkowski functionals. The joint structure quantification can detect formerly
undetected sources. We show how the additional shape information leads to the
increase in sensitivity. We explain the very unique concepts and possibilites
of our analysis compared to a standard counting method in gamma-ray astronomy,
and we present in an outlook further improvements especially for the detection
of diffuse background radiation and generalizations of our technique.
