Name
Abstract | ||
|---|---|---|
Magnetic Resonance Spectroscopy (MRS) provides the biochemical composition of a tissue under study. This information is useful for the in-vivo diagnosis of brain tumours. Prior knowledge of the relative position of the organic compound contributions in the MRS suggests the development of a probabilistic mixture model and its EM-based Maximum Likelihood Estimation for binned and truncated data. Experiments for characterizing and classifying Short Time Echo (STE) spectra from brain tumours are reported. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72849-8_34 | IbPRIA (2) |
Keywords | Field | DocType |
brain tumour,em-based maximum likelihood estimation,magnetic resonance spectra,organic compound contribution,truncated data,relative position,probabilistic mixture model,classifying short time echo,biochemical composition,in-vivo diagnosis,prior knowledge,magnetic resonance spectroscopy,mixture model,maximum likelihood estimate,magnetic resonance | Pattern recognition,Expectation–maximization algorithm,Computer science,Maximum likelihood,Spectral line,Truncation (statistics),Artificial intelligence,Probabilistic logic,Mixture model,Nuclear magnetic resonance spectroscopy,Magnetic resonance imaging | Conference |
Volume | ISSN | Citations |
4478 | 0302-9743 | 1 |
PageRank | References | Authors |
0.44 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan M. Garcia-Gomez | 1 | 1 | 0.44 |
| Montserrat Robles | 2 | 1064 | 58.83 |
| Sabine Huffel | 3 | 4 | 2.90 |
| Alfons Juan-Císcar | 4 | 3 | 2.17 |