Topological Data Analysis (TDA) has emerged as a powerful framework for extracting meaningful structure from complex, high-dimensional data. In particular, persistent homology is widely used for its ability to quantify multiscale topological features while exhibiting robustness to noise.

In this work, we apply persistent homology to hyperspectral images of retinal tissue in order to further investigate Spaceflight Associated Neuro-ocular Syndrome. Hyperspectral imaging captures vast spectral information, but its high dimensionality poses challenges for analysis and interpretation. For each spectral band, we treat pixel intensity as a scalar function and construct a sublevel set filtration of cubical complexes, which provide a natural cell-complex structure for image data. From the resulting persistence diagrams, we derive summary statistics including total persistence and feature counts in dimension 0.

Preliminary results indicate that these persistence-based summaries distinguish between pigmented and albino retinal tissue. Ongoing work focuses on further interpretation of the detected topological structure and the implementation of additional persistence-based methods.