Zigzag persistent homology for processing neuronal images
We apply the ideas of zigzag persistence to determine the objects of interest in stacks of neuronal images, locating and marking different dendrites. In particular, this allows us to recognize some 3D properties of the objects, distinguishing dendrites that cross, but not intersect, in the ambient s...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universidad de La Rioja (UR) |
| Repositorio: | RIUR. Repositorio Institucional de la Universidad de La Rioja |
| OAI Identifier: | oai:dnet:riur________::d93c58c3f97ec4fb55dcc10ff9710d1c |
| Acceso en línea: | https://investigacion.unirioja.es/documentos/5bbc6813b750603269e80207 |
| Access Level: | acceso abierto |
| Palabra clave: | Dendrite reconstruction Homology Neural images |
| Sumario: | We apply the ideas of zigzag persistence to determine the objects of interest in stacks of neuronal images, locating and marking different dendrites. In particular, this allows us to recognize some 3D properties of the objects, distinguishing dendrites that cross, but not intersect, in the ambient space. The algorithms are implemented in a Fiji/ImageJ plugin, usable on two different kinds of images. © 2015ElsevierB.V.Allrightsreserved. |
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