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. 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.
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 FWHM Gaussian kernels (sigma) to the data: sigma=0; sigma=3; sigma��=6; sigma=9; sigma=12. We also apply different thresholds to classify with different number of features.




<h3>
<h3>
Feature sets extracted applying Pearson's correlation
Feature sets extracted applying Pearson's correlation:
</h3>
</h3>
* k=0 -
* sigma=0 -
* k=3 -
* sigma=3 -
* k=6 -
* sigma=6 -
* k=9 -
* sigma=9 -
* k=12 -
* sigma=12 -


<h3>
<h3>
Feature sets extracted applying VBM
Feature sets extracted applying VBM:
</h3>
</h3>
* k=0 -
* sigma=0 -
* k=3 -
* sigma=3 -
* k=6 -
* sigma=6 -
* k=9 -
* sigma=9 -
* k=12 -
* sigma=12 -

Revisión del 12:28 15 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 FWHM Gaussian kernels (sigma) to the data: sigma=0; sigma=3; sigma��=6; sigma=9; sigma=12. We also apply different thresholds to classify with different number of features.


Feature sets extracted applying Pearson's correlation:

  • sigma=0 -
  • sigma=3 -
  • sigma=6 -
  • sigma=9 -
  • sigma=12 -

Feature sets extracted applying VBM:

  • sigma=0 -
  • sigma=3 -
  • sigma=6 -
  • sigma=9 -
  • sigma=12 -
Obtenido de «http://ehu.eus/ccwintco/index.php?title=GIC-experimental-databases/Cocaine_feature_vectors&oldid=14341»