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Neuron detection in stack images: a persistent homology interpretation

Automation and reliability are the two main requirements when computers are applied in Life Sciences. In this paper we report on an application to neuron recognition, an important step in our long-term project of providing software systems to the study of neural morphology and functionality from biomedical images. Our algorithms have been implemented in an ImageJ plugin called NeuronPersistentJ, which has been validated experimentally. The soundness and reliability of our approach are based on the interpretation of our processing methods with respect to persistent homology, a well-known tool in computational mathematics.

preprint2015arXivOpen access
Jónathan HerasGadea MataGermán CuestoJulio RubioMiguel Morales
Computer VisionNeurons and Cognition
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Open access5 authors2 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Jónathan HerasGadea MataGermán CuestoJulio RubioMiguel Morales

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