Diferencia entre revisiones de «GIC-experimental-databases/Cocaine feature vectors»
De Grupo de Inteligencia Computacional (GIC)
| Línea 1: | Línea 1: | ||
== Datasets of features extracted from a database of 124 subjects: 61 healthy controls and 63 cocaine adicted patients == | == Datasets of features extracted from a database of 124 subjects: 61 healthy controls and 63 cocaine adicted patients == | ||
These features are based on two feature extraction pipelines: one based on Pearson's correlation and other based on voxel based morphometry (VBM) methodology. For the moment, we work over the GM voxel intensity values after image preprocessing. | These features are based on two feature extraction pipelines: one based on Pearson's correlation and other based on voxel based morphometry (VBM) methodology. For the moment, we work over the GM voxel intensity values after image preprocessing. We apply different smooth kernels (k) to the data: k=0; k=3; k=6; k=9; k=12. We also apply different thresholds to classify with different number of features. | ||
We apply different smooth kernels (k) to the data: k=0; k=3; k=6; k=9; k=12. | |||
<h3> | <h3> | ||
Revisión del 17:23 13 feb 2013
Datasets of features extracted from a database of 124 subjects: 61 healthy controls and 63 cocaine adicted patients
These features are based on two feature extraction pipelines: one based on Pearson's correlation and other based on voxel based morphometry (VBM) methodology. For the moment, we work over the GM voxel intensity values after image preprocessing. We apply different smooth kernels (k) to the data: k=0; k=3; k=6; k=9; k=12. We also apply different thresholds to classify with different number of features.
Feature sets extracted applying Pearson's correlation
- k=0 -
- k=3 -
- k=6 -
- k=9 -
- k=12 -
Feature sets extracted applying VBM
- k=0 -
- k=3 -
- k=6 -
- k=9 -
- k=12 -
