We present some novel tools for the analysis of blue-noise binary patterns. Unlike most of the existing methods that evaluate the frequency content of a given mask or its lower order statistics, our new metrics characterize the morphological content of a mask that is quantified using simple one-pass filtering. An analytical filter expression is given. As a result, one can balance the structural content of the mask—diagonal, vertical, and horizontal interconnections of the majority (or minority) pixels—at the same level. In addition, it is possible to improve the overall mask quality by prescribing the occurrence of morphological shapes of connected pixels. Examples of morphological analysis are given to demonstrate the different qualities of blue-noise and white-noise patterns.