Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2015 | arXiv: Computer Vision and Pattern Recognition | Computer science,Computational mathematics,Persistent homology,Software system,Automation,Artificial intelligence,Plug-in,Soundness,Neuron recognition,Machine learning |
DocType | Volume | Citations |
Journal | abs/1509.04420 | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jónathan Heras | 1 | 94 | 23.31 |
| Gadea Mata | 2 | 14 | 3.57 |
| Germán Cuesto | 3 | 0 | 0.68 |
| J. Rubio | 4 | 202 | 31.12 |
| Miguel Morales | 5 | 0 | 0.68 |