| Title |
Adaptive Image Enhancement for Tracing 3D Morphologies of Neurons and Brain Vasculatures
|
|---|---|
| Published in |
Neuroinformatics, October 2014
|
| DOI | 10.1007/s12021-014-9249-y |
| Pubmed ID | |
| Authors |
Zhi Zhou, Staci Sorensen, Hongkui Zeng, Michael Hawrylycz, Hanchuan Peng |
| 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 47 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 32% |
| Researcher | 10 | 21% |
| Student > Bachelor | 4 | 9% |
| Other | 4 | 9% |
| Student > Master | 3 | 6% |
| Other | 6 | 13% |
| Unknown | 5 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 10 | 21% |
| Agricultural and Biological Sciences | 8 | 17% |
| Engineering | 8 | 17% |
| Computer Science | 8 | 17% |
| Biochemistry, Genetics and Molecular Biology | 1 | 2% |
| Other | 3 | 6% |
| Unknown | 9 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 October 2014.
All research outputs
#14,598,896
of 22,664,644 outputs
Outputs from Neuroinformatics
#233
of 401 outputs
Outputs of similar age
#139,368
of 255,746 outputs
Outputs of similar age from Neuroinformatics
#4
of 7 outputs
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