Abstract
In this paper, we apply persistent entropy, a novel topological statistic, for characterization of images of epithelial tissues. We have found out that persistent entropy is able to summarize topological and geometric information encoded by \(\alpha \)-complexes and persistent homology. After using some statistical tests, we can guarantee the existence of significant differences in the studied tissues.
Partially supported by MINECO, FEDER/UE under grant MTM2015-67072-P. Authors names are listed in alphabetical order.
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Atienza, N., Escudero, L.M., Jimenez, M.J., Soriano-Trigueros, M. (2019). Characterising Epithelial Tissues Using Persistent Entropy. In: Marfil, R., CalderĂłn, M., DĂaz del RĂo, F., Real, P., Bandera, A. (eds) Computational Topology in Image Context. CTIC 2019. Lecture Notes in Computer Science(), vol 11382. Springer, Cham. https://doi.org/10.1007/978-3-030-10828-1_14
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