Conditions for Morphology-Based Topological Filtrations and Applications to Firn Data Analysis

Persistent homology (PH), a key tool in topological data analysis (TDA), captures global topological features of digital images through \emph{topological filtrations}.

Alternatively, mathematical morphology (MM), rooted in set theory and lattice theory, provides operations such as opening and closing to modify local geometric structures in digital images.

This motivates incorporating local geometric information into a PH framework via morphological filtrations, yielding an MM-based PH framework.

However, the validity of such filtrations depends on the absorption property of MM operations, which may fail for arbitrary structuring elements, the components defining MM operators.

To address this issue, we introduce shift inclusion as a sufficient condition for ensuring absorption, provide a formal proof, and demonstrate its utility in pore-structure analysis, highlighting the synergy between MM and PH for image and scientific data analysis.