Name
Title | ||
|---|---|---|
Automated reconstruction of neuronal morphology based on local geometrical and global structural models. |
Abstract | ||
|---|---|---|
Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s12021-011-9120-3 | Neuroinformatics |
Keywords | Field | DocType |
diadem · neuron tracing · tube models · tree structure reconstruction · 3d microscopy,neuronal morphology,tree structure | Computer vision,Data set,Software design,Computer science,Image properties,Software,Artificial intelligence,Digital reconstruction,Software verification and validation,Machine learning | Journal |
Volume | Issue | ISSN |
9 | 2-3 | 1559-0089 |
Citations | PageRank | References |
46 | 1.68 | 13 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ting Zhao | 1 | 46 | 1.68 |
| Jun Xie | 2 | 46 | 1.68 |
| Fernando Amat | 3 | 64 | 3.27 |
| Nathan Clack | 4 | 46 | 1.68 |
| Parvez Ahammad | 5 | 250 | 14.51 |
| Hanchuan Peng | 6 | 3930 | 182.27 |
| Fuhui Long | 7 | 304 | 19.27 |
| Eugene Myers | 8 | 3164 | 496.92 |