Name
Title | ||
|---|---|---|
Adaptive Image Enhancement for Tracing 3D Morphologies of Neurons and Brain Vasculatures. |
Abstract | ||
|---|---|---|
It is important to digitally reconstruct the 3D morphology of neurons and brain vasculatures. A number of previous methods have been proposed to automate the reconstruction process. However, in many cases, noise and low signal contrast with respect to the image background still hamper our ability to use automation methods directly. Here, we propose an adaptive image enhancement method specifically designed to improve the signal-to-noise ratio of several types of individual neurons and brain vasculature images. Our method is based on detecting the salient features of fibrous structures, e.g. the axon and dendrites combined with adaptive estimation of the optimal context windows where such saliency would be detected. We tested this method for a range of brain image datasets and imaging modalities, including bright-field, confocal and multiphoton fluorescent images of neurons, and magnetic resonance angiograms. Applying our adaptive enhancement to these datasets led to improved accuracy and speed in automated tracing of complicated morphology of neurons and vasculatures. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s12021-014-9249-y | Neuroinformatics |
Keywords | Field | DocType |
Adaptive image enhancement, Anisotropic filtering, Gray-scale distance transformation, 3D neuron reconstruction, Vaa3D | Computer vision,Computer science,Salience (neuroscience),Imaging modalities,Anisotropic filtering,Axon,Artificial intelligence,Confocal,Machine learning,Tracing,Magnetic resonance imaging | Journal |
Volume | Issue | ISSN |
13 | 2 | 1559-0089 |
Citations | PageRank | References |
13 | 0.60 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi Zhou | 1 | 13 | 0.60 |
| Staci Sorensen | 2 | 13 | 0.60 |
| Hongkui Zeng | 3 | 14 | 1.97 |
| Michael Hawrylycz | 4 | 145 | 11.11 |
| Hanchuan Peng | 5 | 3930 | 182.27 |