GIC-experimental-databases/OASIS VBM feature vectors - Historial de revisiones

Manuel.grana en 20:28 29 abr 2016

2016-04-29T20:28:39Z

← Revisión anterior		Revisión del 22:28 29 abr 2016
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	;; ~~Reference~~:		;; References:

	:A. Savio, M.T. García-Sebastián, D. Chyzyk, C. Hernandez, M. Graña, A. Sistiaga, A. López de Munain, J. Villanúa,		:A. Savio, M.T. García-Sebastián, D. Chyzyk, C. Hernandez, M. Graña, A. Sistiaga, A. López de Munain, J. Villanúa,
Línea 39:		Línea 39:
	:(http://www.sciencedirect.com/science/article/pii/S0010482511001065)		:(http://www.sciencedirect.com/science/article/pii/S0010482511001065)
	:Keywords: Alzheimer's disease; Classification; Feature extraction; Structural MRI; Myotonic distrophy of type 1		:Keywords: Alzheimer's disease; Classification; Feature extraction; Structural MRI; Myotonic distrophy of type 1

			;Darya Chyzhyk, Alexandre Savio, Manuel Graña,
			:Evolutionary ELM wrapper feature selection for Alzheimer's disease CAD on anatomical brain MRI pdf
			:Neurocomputing, Volume 128, 27 March 2014, Pages 73-80,
			:ISSN 0925-2312, http://dx.doi.org/10.1016/j.neucom.2013.01.065.

Alexsavio en 10:39 22 oct 2013

2013-10-22T10:39:10Z

← Revisión anterior		Revisión del 12:39 22 oct 2013
Línea 27:		Línea 27:
	;; Other files of interest:		;; Other files of interest:

	[[media:oasis_vbm_matlab_source.zip \| Matlab source and subjects_list.txt]]		:[[media:oasis_vbm_matlab_source.zip \| Matlab source and subjects_list.txt]]

	:[[List of 98 OASIS females]]		:[[List of 98 OASIS females]]
Línea 34:		Línea 34:
	;; Reference:		;; Reference:

	A. Savio, M.T. García-Sebastián, D. Chyzyk, C. Hernandez, M. Graña, A. Sistiaga, A. López de Munain, J. Villanúa,		: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''		:''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.		: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)		:(http://www.sciencedirect.com/science/article/pii/S0010482511001065)
	Keywords: Alzheimer's disease; Classification; Feature extraction; Structural MRI; Myotonic distrophy of type 1		:Keywords: Alzheimer's disease; Classification; Feature extraction; Structural MRI; Myotonic distrophy of type 1

Alexsavio en 10:38 22 oct 2013

2013-10-22T10:38:47Z

← Revisión anterior		Revisión del 12:38 22 oct 2013
Línea 28:		Línea 28:

	[[media:oasis_vbm_matlab_source.zip \| Matlab source and subjects_list.txt]]		[[media:oasis_vbm_matlab_source.zip \| Matlab source and subjects_list.txt]]

			:[[List of 98 OASIS females]]

Alexsavio en 10:36 22 oct 2013

2013-10-22T10:36:01Z

← Revisión anterior		Revisión del 12:36 22 oct 2013
Línea 23:		Línea 23:
	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:

Alexsavio en 10:35 22 oct 2013

2013-10-22T10:35:52Z

← Revisión anterior		Revisión del 12:35 22 oct 2013
Línea 27:		Línea 27:

	[[media:oasis_vbm_matlab_source.zip \| Matlab source and subjects_list.txt]]		[[media:oasis_vbm_matlab_source.zip \| Matlab source and subjects_list.txt]]

Alexsavio en 10:35 22 oct 2013

2013-10-22T10:35:39Z

← Revisión anterior		Revisión del 12:35 22 oct 2013
Línea 26:		Línea 26:
	;; Other files of interest:		;; Other files of interest:

	[[media:~~subject_list~~.txt]]		[[media:oasis_vbm_matlab_source.zip \| Matlab source and subjects_list.txt]]

	~~[[media:extractVoxelIntensitiesWithinClusters.m]]~~

	~~[[media:extractVoxelIntensitiesFromEachCluster.m]]~~

	~~[[media:extractMeanAndStdDevFromEachCluster.m]]~~

	~~[[media:extractGMProportionFromEachCluster.m~~]]

Alexsavio en 10:32 22 oct 2013

2013-10-22T10:32:47Z

@@ Línea 23: / Línea 23: @@
 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.
 ;; Reference:

Alexsavio en 10:30 22 oct 2013

2013-10-22T10:30:24Z

← Revisión anterior		Revisión del 12:30 22 oct 2013
Línea 6:		Línea 6:

	CONTROLES is the raw data of the control subjects, they have label -1		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		PACIENTES is the raw data of the patient subjects, they have label 1

Alexsavio en 10:30 22 oct 2013

2013-10-22T10:30:15Z

← Revisión anterior		Revisión del 12:30 22 oct 2013
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	[[media:oasis_vbm.zip \| Experimental database of features obtained from the VBM of a subset of the OASIS database for the classification of Alzheimer's Disease patients versus controls with example source code]]		[[media:oasis_vbm.zip \| Experimental database of features obtained from the VBM of a subset of the OASIS database for the classification of Alzheimer's Disease patients versus controls with example source code]]

	CONTROLES is the raw data of the control subjects, they have label -1		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
			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).		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.,:		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,:,:)))])
			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).		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:		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);		[C P] = extractVoxelIntensitiesWithinClusters (female_crystal_brain_cov0, CONTROLES, PACIENTES);

	Where C are the controls voxels within the VBM clusters and P the patients ones.		Where C are the controls voxels within the VBM clusters and P the patients ones.

Alexsavio en 10:29 22 oct 2013

2013-10-22T10:29:49Z

← Revisión anterior		Revisión del 12:29 22 oct 2013
Línea 23:		Línea 23:
	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.

	Reference:		;; Reference:

	A. Savio, M.T. García-Sebastián, D. Chyzyk, C. Hernandez, M. Graña, A. Sistiaga, A. López de Munain, J. Villanúa,		A. Savio, M.T. García-Sebastián, D. Chyzyk, C. Hernandez, M. Graña, A. Sistiaga, A. López de Munain, J. Villanúa,