Diferencia entre revisiones de «GIC-experimental-databases/OASIS VBM feature vectors»
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Otherwise, if you don't want to use the VBM result you could perform another feature extraction of yourself directly on CONTROLES and PACIENTES. | Otherwise, if you don't want to use the VBM result you could perform another feature extraction of yourself directly on CONTROLES and PACIENTES. | ||
I imagine you would have to reshape it, flattening their 2nd, 3rd and 4th dimesions into one. | I imagine you would have to reshape it, flattening their 2nd, 3rd and 4th dimesions into one. | ||
;; Other files of interest: | ;; Other files of interest: | ||
Revisión del 12:36 22 oct 2013
CONTROLES is the raw data of the control subjects, they have label -1 PACIENTES is the raw data of the patient subjects, they have label 1
If you read the first lines of Diverse_AdaBoost_LVQ_MeanAndStdDev.m you will see that I call the function that I am attaching (extractMeanAndStdDevFromEachCluster.m).
These data are grey matter segmentations as you can see with, e.g.,:
imshow (reshape(PACIENTES(1,60,:,:),91, 91), [min(min(PACIENTES(1,60,:,:))) max(max(PACIENTES(1,60,:,:)))])
female_crystal_brain_cov0 are corrected p-values of a typical statistical test in with anatomical brain MRI in neuroscience called Voxel-based morphometry (VBM).
To use them the same way you used the previous dataset, please use one of the functions I attached in this way:
[C P] = extractVoxelIntensitiesWithinClusters (female_crystal_brain_cov0, CONTROLES, PACIENTES);
Where C are the controls voxels within the VBM clusters and P the patients ones.
Otherwise, if you don't want to use the VBM result you could perform another feature extraction of yourself directly on CONTROLES and PACIENTES. I imagine you would have to reshape it, flattening their 2nd, 3rd and 4th dimesions into one.
- Other files of interest
Matlab source and subjects_list.txt
- Reference
A. Savio, M.T. García-Sebastián, D. Chyzyk, C. Hernandez, M. Graña, A. Sistiaga, A. López de Munain, J. Villanúa, Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI Computers in Biology and Medicine, Volume 41, Issue 8, August 2011, Pages 600-610, ISSN 0010-4825, http://dx.doi.org/10.1016/j.compbiomed.2011.05.010. (http://www.sciencedirect.com/science/article/pii/S0010482511001065) Keywords: Alzheimer's disease; Classification; Feature extraction; Structural MRI; Myotonic distrophy of type 1
