Diferencia entre revisiones de «GIC-experimental-databases/Cocaine feature vectors»

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== 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.   
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.   


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Revisión del 17:20 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.

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 -