Name
Abstract | ||
|---|---|---|
Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D EMD) provides means to analyze such images. It extracts characteristic textures from these images which may be fed into powerful classifiers trained to group these textures into several classes depending on the problem at hand. The study investigates the potential use of 2D EEMD in combination with proper classifiers to form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from a dementia are taken to illustrate this ability. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1142/S1793536914500046 | ADVANCES IN DATA SCIENCE AND ADAPTIVE ANALYSIS |
Keywords | Field | DocType |
Multi-dimensional empirical mode decomposition, positron emission tomography, support vector machine, random forest, dementias | CAD,Support vector machine,Computer-aided diagnosis,Functional imaging,Artificial intelligence,Positron emission tomography,Random forest,Machine learning,Mathematics,Hilbert–Huang transform | Journal |
Volume | Issue | ISSN |
6 | 1 | 2424-922X |
Citations | PageRank | References |
3 | 0.41 | 38 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Neubauer | 1 | 3 | 0.41 |
| Ana Maria Tomé | 2 | 163 | 30.42 |
| Andreas Kodewitz | 3 | 7 | 1.52 |
| J. M. Górriz | 4 | 570 | 54.40 |
| Carlos García Puntonet | 5 | 107 | 25.86 |
| Elmar Wolfgang Lang | 6 | 260 | 36.10 |