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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, November 2017
|
| DOI | 10.3389/fninf.2017.00066 |
| Pubmed ID | |
| Authors |
Diego Castillo-Barnes, Ignacio Peis, Francisco J. Martínez-Murcia, Fermín Segovia, Ignacio A. Illán, Juan M. Górriz, Javier Ramírez, Diego Salas-Gonzalez |
| Abstract |
A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, intensity histograms of White Matter and Gray Matter are not symmetric and they exhibit heavy tails. In this work, we present a hidden Markov random field model with expectation maximization (EM-HMRF) modeling the components using the α-stable distribution. The proposed model is a generalization of the widely used EM-HMRF algorithm with Gaussian distributions. We test the α-stable EM-HMRF model in synthetic data and brain MRI data. The proposed methodology presents two main advantages: Firstly, it is more robust to outliers. Secondly, we obtain similar results than using Gaussian when the Gaussian assumption holds. This approach is able to model the spatial dependence between neighboring voxels in tomographic brain MRI. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 22% |
| Professor > Associate Professor | 2 | 22% |
| Researcher | 2 | 22% |
| Professor | 1 | 11% |
| Student > Doctoral Student | 1 | 11% |
| Other | 0 | 0% |
| Unknown | 1 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 2 | 22% |
| Business, Management and Accounting | 1 | 11% |
| Mathematics | 1 | 11% |
| Neuroscience | 1 | 11% |
| Computer Science | 1 | 11% |
| Other | 0 | 0% |
| Unknown | 3 | 33% |
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 23 November 2018.
All research outputs
#14,960,072
of 23,009,818 outputs
Outputs from Frontiers in Neuroinformatics
#518
of 753 outputs
Outputs of similar age
#251,021
of 437,742 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#13
of 14 outputs
Altmetric has tracked 23,009,818 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 753 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.