alexandre savio | University of the Basque Country, Euskal Herriko Unibertsitatea (original) (raw)

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Research paper thumbnail of Supervised classification using deformation-based features for Alzheimer's disease detection on the OASIS cross-sectional database

Abstract. In the last 10 years, detection of Alzheimer's disease based on brain T1-weighted Magne... more Abstract. In the last 10 years, detection of Alzheimer's disease based on brain T1-weighted Magnetic Resonance Imaging (MRI) have been a highly sought goal in the neuroscientific community. However, the methods were assessed on different datasets and not always publicly available ones making reproducibility and validation impossible.

Research paper thumbnail of Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI

Dementia is a growing concern due to the aging process of the western societies. Non-invasive det... more Dementia is a growing concern due to the aging process of the western societies. Non-invasive detection is therefore a high priority research endeavor. In this paper we report results of classification systems applied to the feature vectors obtained by a feature extraction method computed on structural magnetic resonance imaging (sMRI) volumes for the detection of two neurological disorders with cognitive impairment: myotonic dystrophy of type 1 (MD1) and Alzheimer disease (AD).

Research paper thumbnail of Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation

Research paper thumbnail of Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems

Research paper thumbnail of A lattice computing approach for on-line fMRI analysis

Image and Vision …, Jan 1, 2010

We introduce an approach to fMRI analysis based on the Endmember Induction Heuristic Algorithm (E... more We introduce an approach to fMRI analysis based on the Endmember Induction Heuristic Algorithm (EIHA). This algorithm uses the Lattice Associative Memory (LAM) to detect Lattice Independent vectors, which can be assumed to be Affine Independent, and therefore candidates to be the endmembers of the data. Induced endmembers are used to compute the activation levels of voxels as result of an unmixing process. The endmembers correspond to diverse activation patterns, one of these activation patterns corresponds to the resting state of the neuronal tissue. The on-line working of the algorithm does not need neither a previous training process nor a priori models of the data. Results on a case study compare with the results given by the state of art SPM software.

Research paper thumbnail of On the use of morphometry based features for Alzheimer's disease detection on MRI

Bio-Inspired Systems: …, Jan 1, 2009

We have studied feature extraction processes for the detection of Alzheimer's disease on brain Ma... more We have studied feature extraction processes for the detection of Alzheimer's disease on brain Magnetic Resonance Imaging (MRI) based on Voxel-based morphometry (VBM). The clusters of voxel locations detected by the VBM were applied to select the voxel intensity values upon which the classication features were computed. We have explored the use of the data from the original MRI volumes and the GM segmentation volumes. In this paper, we apply the Support Vector Machine (SVM) algorithm to perform classication of patients with mild Alzheimer's disease vs. control subjects. The study has been performed on MRI volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies (OASIS) database, which is a large number of subjects compared to current reported studies. 1

Research paper thumbnail of Classification results of artificial neural networks for Alzheimer's disease detection

Proceedings of the …, Jan 1, 2009

Detection of Alzheimer's disease on brain Magnetic Resonance Imaging (MRI) is a highly sought goa... more Detection of Alzheimer's disease on brain Magnetic Resonance Imaging (MRI) is a highly sought goal in the Neurosciences. We used four dierent models of Articial Neural Networks (ANN): Backpropagation (BP), Radial Basis Networks (RBF), Learning Vector Quantization Networks (LVQ) and Probabilistic Neural Networks (PNN) to perform classication of patients of mild Alzheimer's disease vs. control subjects. Features are extracted from the brain volume data using Voxelbased Morphometry (VBM) detection clusters. The voxel location detection clusters given by the VBM were applied to select the voxel values upon which the classication features were computed. We have evaluated feature vectors computed from the GM segmentation volumes using the VBM clusters as voxel selection masks. The study has been performed on MRI volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies (OASIS) database, which is a large number of subjects compared to current reported studies.

Research paper thumbnail of Results of an adaboost approach on alzheimer's disease detection on mri

… Applications in Artificial …, Jan 1, 2009

In this paper we explore the use of the Voxel-based Morphometry (VBM) detection clusters to guide... more In this paper we explore the use of the Voxel-based Morphometry (VBM) detection clusters to guide the feature extraction processes for the detection of Alzheimer's disease on brain Magnetic Resonance Imaging (MRI). The voxel location detection clusters given by the VBM were applied to select the voxel values upon which the classication features were computed. We have evaluated feature vectors computed over the data from the original MRI volumes and from the GM segmentation volumes, using the VBM clusters as voxel selection masks. We use the Support Vector Machine (SVM) algorithm to perform classication of patients with mild Alzheimer's disease vs. control subjects. We have also considered combinations of isolated cluster based classiers and an Adaboost strategy applied to the SVM built on the feature vectors. The study has been performed on MRI volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies (OASIS) database, which is a large number of subjects compared to current reported studies. Results are moderately encouraging, as we can obtain up to 85% accuracy with the Adaboost strategy in a 10-fold cross-validation.

Research paper thumbnail of Feature extraction from structural MRI images based on VBM: data from OASIS database

Research paper thumbnail of A Comparison of VBM Results by SPM, ICA and LICA

Hybrid Artificial Intelligence Systems, Jan 1, 2010

Abstract. Lattice Independent Component Analysis (LICA) approach consists of a detection of indep... more Abstract. Lattice Independent Component Analysis (LICA) approach consists of a detection of independent vectors in the morphological or lattice theoretic sense that are the basis for a linear decomposition of the data. We apply it in this paper to a Voxel Based Morphometry (VBM) study on Alzheimer's disease (AD) patients extracted from a well known public database. The approach is compared to SPM and Independent Component Analysis results.

Research paper thumbnail of NEURAL CLASSIFIERS FOR SCHIZOPHRENIA DIAGNOSTIC SUPPORT ON DIFFUSION IMAGING DATA

Research paper thumbnail of Hybrid dendritic computing with kernel-LICA applied to Alzheimer's disease detection in MRI

Neurocomputing, Jan 1, 2011

Dendritic computing has been proved to produce perfect approximation of any data distribution. Th... more Dendritic computing has been proved to produce perfect approximation of any data distribution. This result guarantees perfect accuracy training. However, we have found great performance degradation when tested on conventional k-fold cross-validation schemes. In this paper we propose to use Lattice Independent Component Analysis (LICA) and the Kernel transformation of the data as an appropriate feature extraction that improves the generalization of dendritic computing classifiers. We obtain a big increase in classification performance applying with this schema over a database of features extracted from Magnetic Resonance Imaging (MRI) including Alzheimer's disease (AD) patients and control subjects. (M. Grañ a). 1

Research paper thumbnail of Hybrid Artificial Intelligence Systems: 5th International Conference, HAIS 2010, San Sebastián, Spain, June 23-25, 2010: Proceedings

Research paper thumbnail of Deformation Based Features for Alzheimer's Disease Detection with Linear SVM

Hybrid Artificial Intelligent Systems, Jan 1, 2011

Detection of Alzheimer's disease over brain Magnetic Resonance Imaging (MRI) data is a priority g... more Detection of Alzheimer's disease over brain Magnetic Resonance Imaging (MRI) data is a priority goal in the Neurosciences. In previous works we have studied the accuracy of feature vectors obtained from VBM studies of the MRI data. In this paper we report results working on deformation based features, obtained from the deformation vectors computed by non-linear registration processes. Feature selection is based on the correlation between the scalar values computed from the deformation maps and the control variable. Results with linear kernel SVM reach accuracies comparable to previous best results.

Research paper thumbnail of Alzheimer´ s disease detection on MRI, VBM and standard classifiers

JIC'09, Jan 1, 2009

Información del artículo Alzheimer´s disease detection on MRI, VBM and standard classifiers.

Research paper thumbnail of Supervised classification using deformation-based features for Alzheimer's disease detection on the OASIS cross-sectional database

Abstract. In the last 10 years, detection of Alzheimer's disease based on brain T1-weighted Magne... more Abstract. In the last 10 years, detection of Alzheimer's disease based on brain T1-weighted Magnetic Resonance Imaging (MRI) have been a highly sought goal in the neuroscientific community. However, the methods were assessed on different datasets and not always publicly available ones making reproducibility and validation impossible.

Research paper thumbnail of Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI

Dementia is a growing concern due to the aging process of the western societies. Non-invasive det... more Dementia is a growing concern due to the aging process of the western societies. Non-invasive detection is therefore a high priority research endeavor. In this paper we report results of classification systems applied to the feature vectors obtained by a feature extraction method computed on structural magnetic resonance imaging (sMRI) volumes for the detection of two neurological disorders with cognitive impairment: myotonic dystrophy of type 1 (MD1) and Alzheimer disease (AD).

Research paper thumbnail of Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation

Research paper thumbnail of Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems

Research paper thumbnail of A lattice computing approach for on-line fMRI analysis

Image and Vision …, Jan 1, 2010

We introduce an approach to fMRI analysis based on the Endmember Induction Heuristic Algorithm (E... more We introduce an approach to fMRI analysis based on the Endmember Induction Heuristic Algorithm (EIHA). This algorithm uses the Lattice Associative Memory (LAM) to detect Lattice Independent vectors, which can be assumed to be Affine Independent, and therefore candidates to be the endmembers of the data. Induced endmembers are used to compute the activation levels of voxels as result of an unmixing process. The endmembers correspond to diverse activation patterns, one of these activation patterns corresponds to the resting state of the neuronal tissue. The on-line working of the algorithm does not need neither a previous training process nor a priori models of the data. Results on a case study compare with the results given by the state of art SPM software.

Research paper thumbnail of On the use of morphometry based features for Alzheimer's disease detection on MRI

Bio-Inspired Systems: …, Jan 1, 2009

We have studied feature extraction processes for the detection of Alzheimer's disease on brain Ma... more We have studied feature extraction processes for the detection of Alzheimer's disease on brain Magnetic Resonance Imaging (MRI) based on Voxel-based morphometry (VBM). The clusters of voxel locations detected by the VBM were applied to select the voxel intensity values upon which the classication features were computed. We have explored the use of the data from the original MRI volumes and the GM segmentation volumes. In this paper, we apply the Support Vector Machine (SVM) algorithm to perform classication of patients with mild Alzheimer's disease vs. control subjects. The study has been performed on MRI volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies (OASIS) database, which is a large number of subjects compared to current reported studies. 1

Research paper thumbnail of Classification results of artificial neural networks for Alzheimer's disease detection

Proceedings of the …, Jan 1, 2009

Detection of Alzheimer's disease on brain Magnetic Resonance Imaging (MRI) is a highly sought goa... more Detection of Alzheimer's disease on brain Magnetic Resonance Imaging (MRI) is a highly sought goal in the Neurosciences. We used four dierent models of Articial Neural Networks (ANN): Backpropagation (BP), Radial Basis Networks (RBF), Learning Vector Quantization Networks (LVQ) and Probabilistic Neural Networks (PNN) to perform classication of patients of mild Alzheimer's disease vs. control subjects. Features are extracted from the brain volume data using Voxelbased Morphometry (VBM) detection clusters. The voxel location detection clusters given by the VBM were applied to select the voxel values upon which the classication features were computed. We have evaluated feature vectors computed from the GM segmentation volumes using the VBM clusters as voxel selection masks. The study has been performed on MRI volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies (OASIS) database, which is a large number of subjects compared to current reported studies.

Research paper thumbnail of Results of an adaboost approach on alzheimer's disease detection on mri

… Applications in Artificial …, Jan 1, 2009

In this paper we explore the use of the Voxel-based Morphometry (VBM) detection clusters to guide... more In this paper we explore the use of the Voxel-based Morphometry (VBM) detection clusters to guide the feature extraction processes for the detection of Alzheimer's disease on brain Magnetic Resonance Imaging (MRI). The voxel location detection clusters given by the VBM were applied to select the voxel values upon which the classication features were computed. We have evaluated feature vectors computed over the data from the original MRI volumes and from the GM segmentation volumes, using the VBM clusters as voxel selection masks. We use the Support Vector Machine (SVM) algorithm to perform classication of patients with mild Alzheimer's disease vs. control subjects. We have also considered combinations of isolated cluster based classiers and an Adaboost strategy applied to the SVM built on the feature vectors. The study has been performed on MRI volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies (OASIS) database, which is a large number of subjects compared to current reported studies. Results are moderately encouraging, as we can obtain up to 85% accuracy with the Adaboost strategy in a 10-fold cross-validation.

Research paper thumbnail of Feature extraction from structural MRI images based on VBM: data from OASIS database

Research paper thumbnail of A Comparison of VBM Results by SPM, ICA and LICA

Hybrid Artificial Intelligence Systems, Jan 1, 2010

Abstract. Lattice Independent Component Analysis (LICA) approach consists of a detection of indep... more Abstract. Lattice Independent Component Analysis (LICA) approach consists of a detection of independent vectors in the morphological or lattice theoretic sense that are the basis for a linear decomposition of the data. We apply it in this paper to a Voxel Based Morphometry (VBM) study on Alzheimer's disease (AD) patients extracted from a well known public database. The approach is compared to SPM and Independent Component Analysis results.

Research paper thumbnail of NEURAL CLASSIFIERS FOR SCHIZOPHRENIA DIAGNOSTIC SUPPORT ON DIFFUSION IMAGING DATA

Research paper thumbnail of Hybrid dendritic computing with kernel-LICA applied to Alzheimer's disease detection in MRI

Neurocomputing, Jan 1, 2011

Dendritic computing has been proved to produce perfect approximation of any data distribution. Th... more Dendritic computing has been proved to produce perfect approximation of any data distribution. This result guarantees perfect accuracy training. However, we have found great performance degradation when tested on conventional k-fold cross-validation schemes. In this paper we propose to use Lattice Independent Component Analysis (LICA) and the Kernel transformation of the data as an appropriate feature extraction that improves the generalization of dendritic computing classifiers. We obtain a big increase in classification performance applying with this schema over a database of features extracted from Magnetic Resonance Imaging (MRI) including Alzheimer's disease (AD) patients and control subjects. (M. Grañ a). 1

Research paper thumbnail of Hybrid Artificial Intelligence Systems: 5th International Conference, HAIS 2010, San Sebastián, Spain, June 23-25, 2010: Proceedings

Research paper thumbnail of Deformation Based Features for Alzheimer's Disease Detection with Linear SVM

Hybrid Artificial Intelligent Systems, Jan 1, 2011

Detection of Alzheimer's disease over brain Magnetic Resonance Imaging (MRI) data is a priority g... more Detection of Alzheimer's disease over brain Magnetic Resonance Imaging (MRI) data is a priority goal in the Neurosciences. In previous works we have studied the accuracy of feature vectors obtained from VBM studies of the MRI data. In this paper we report results working on deformation based features, obtained from the deformation vectors computed by non-linear registration processes. Feature selection is based on the correlation between the scalar values computed from the deformation maps and the control variable. Results with linear kernel SVM reach accuracies comparable to previous best results.

Research paper thumbnail of Alzheimer´ s disease detection on MRI, VBM and standard classifiers

JIC'09, Jan 1, 2009

Información del artículo Alzheimer´s disease detection on MRI, VBM and standard classifiers.