Diferencia entre revisiones de «GIC-experimental-databases/OASIS deformation feature vectors»
De Grupo de Inteligencia Computacional (GIC)
Sin resumen de edición |
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These features are based on deformation measures (displacement vector magnitudes and Jacobian determinant of gradient matrices) of a custom template made with all the 98 subjects registered to each subject. | These features are based on deformation measures (displacement vector magnitudes and Jacobian determinant of gradient matrices) of a custom template made with all the 98 subjects registered to each subject. | ||
Some feature sets doesn't exist because the correlation values were lower than the percentile limit. | |||
<br/> | <br/> | ||
Reference paper with results on these datasets | |||
:Alexandre Savio, Manuel Graña, Jorge Villanúa | |||
:[[media:ASavio-HAIS-2011.pdf|Deformation based features for Alzheimer's disease detection with linear SVM]] | |||
:Hybrid Artificial Intelligence Systems, 6th International Conference (HAIS 2011) - HAIS 2011, Part II, LNAI 6679 proceedings, p.336-343. Springer, Heidelberg (2011) | |||
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Pipelines trying to explain how these features were extracted: | |||
</h3> | |||
* [[media:Registration_deformation_calculation.png | Obtaining the measures of the displacement vectors. ]] | |||
* [[media:Correlation_measure_calculation.png | Obtaining the correlation values from the displacement measures. ]] | |||
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</h3> | </h3> | ||
* [[media:DM_prctile990.zip | Feature sets of | * [[media:DM_prctile990.zip | Feature sets of Pearson, Spearman and Kendall correlation measures over a 0.990 percentile]] | ||
* [[media:DM_prctile995.zip | Feature sets of | * [[media:DM_prctile995.zip | Feature sets of Pearson, Spearman and Kendall correlation measures over a 0.995 percentile]] | ||
* [[media:DM_prctile999.zip | Feature sets of | * [[media:DM_prctile999.zip | Feature sets of Spearman and Kendall correlation measures over a 0.999 percentile]] | ||
<h3> | <h3> | ||
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</h3> | </h3> | ||
* [[media:JD_prctile990.zip | Feature sets of | * [[media:JD_prctile990.zip | Feature sets of Pearson, Spearman and Kendall correlation measures over a 0.990 percentile]] | ||
* [[media:JD_prctile995.zip | Feature sets of | * [[media:JD_prctile995.zip | Feature sets of Spearman and Kendall correlation measures over a 0.995 percentile]] | ||
* [[media:JD_prctile999.zip | Feature sets of | * [[media:JD_prctile999.zip | Feature sets of Spearman and Kendall correlation measures over a 0.999 percentile]] | ||
Contact: Alexandre Savio. | |||
Revisión actual - 01:09 3 oct 2013
Datasets of features extracted from the subset of 98 females from OASIS
These features are based on deformation measures (displacement vector magnitudes and Jacobian determinant of gradient matrices) of a custom template made with all the 98 subjects registered to each subject.
Some feature sets doesn't exist because the correlation values were lower than the percentile limit.
Reference paper with results on these datasets
- Alexandre Savio, Manuel Graña, Jorge Villanúa
- Deformation based features for Alzheimer's disease detection with linear SVM
- Hybrid Artificial Intelligence Systems, 6th International Conference (HAIS 2011) - HAIS 2011, Part II, LNAI 6679 proceedings, p.336-343. Springer, Heidelberg (2011)
Pipelines trying to explain how these features were extracted:
- Obtaining the measures of the displacement vectors.
- Obtaining the correlation values from the displacement measures.
Feature sets extracted from transformation displacement magnitudes (DM)
- Feature sets of Pearson, Spearman and Kendall correlation measures over a 0.990 percentile
- Feature sets of Pearson, Spearman and Kendall correlation measures over a 0.995 percentile
- Feature sets of Spearman and Kendall correlation measures over a 0.999 percentile
Feature sets extracted from transformation gradient Jacobian matrices determinant (JD)
- Feature sets of Pearson, Spearman and Kendall correlation measures over a 0.990 percentile
- Feature sets of Spearman and Kendall correlation measures over a 0.995 percentile
- Feature sets of Spearman and Kendall correlation measures over a 0.999 percentile
Contact: Alexandre Savio.
