2021
DOI: 10.3390/math9212726
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Space: The Re-Visioning Frontier of Biological Image Analysis with Graph Theory, Computational Geometry, and Spatial Statistics

Abstract: Every biological image contains quantitative data that can be used to test hypotheses about how patterns were formed, what entities are associated with one another, and whether standard mathematical methods inform our understanding of biological phenomena. In particular, spatial point distributions and polygonal tessellations are particularly amendable to analysis with a variety of graph theoretic, computational geometric, and spatial statistical tools such as: Voronoi polygons; Delaunay triangulations; perpen… Show more

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Cited by 2 publications
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“…For example, when partitioning a town (space) into school districts (polygons), a Voronoi partitioning guarantees that all students living within a district are closer to that district’s school (center) than any other school, thus minimizing distances traveled. While many biological structures found in nature also seem to resemble Voronoi diagrams [31], in most of these examples (e.g., giraffe skin patterns [32] or secondary veins of dragonflies [33]) only the polygon boundaries but not the centers are present (Supplementary Text Section 1). Thus, rigorously testing whether these examples are naturally occurring Voronoi diagrams is not straight-forward.…”
Section: Introductionmentioning
confidence: 99%
“…For example, when partitioning a town (space) into school districts (polygons), a Voronoi partitioning guarantees that all students living within a district are closer to that district’s school (center) than any other school, thus minimizing distances traveled. While many biological structures found in nature also seem to resemble Voronoi diagrams [31], in most of these examples (e.g., giraffe skin patterns [32] or secondary veins of dragonflies [33]) only the polygon boundaries but not the centers are present (Supplementary Text Section 1). Thus, rigorously testing whether these examples are naturally occurring Voronoi diagrams is not straight-forward.…”
Section: Introductionmentioning
confidence: 99%