D Salas-Gonzalez

Summary

Affiliation: University of Granada
Country: Spain

Publications

  1. ncbi Feature selection using factor analysis for Alzheimer's diagnosis using 18F-FDG PET images
    D Salas-Gonzalez
    Department of Signal Theory, Networking and Communications, ETSIIT 18071, University of Granada, Granada, Spain
    Med Phys 37:6084-95. 2010
  2. ncbi Parameterization of the distribution of white and grey matter in MRI using the α-stable distribution
    D Salas-Gonzalez
    Department of Signal Theory, Networking and Communications, University of Granada, Spain Electronic address
    Comput Biol Med 43:559-67. 2013
  3. ncbi Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees
    D Salas-Gonzalez
    Department of Signal Theory Networking and Communications, ETSIIT, University of Granada, Spain
    Phys Med Biol 55:2807-17. 2010
  4. ncbi Analysis of SPECT brain images for the diagnosis of Alzheimer's disease using moments and support vector machines
    Diego Salas-Gonzalez
    Department of Signal Theory, Networking and Communications, ETSIIT 18071, University of Granada, Spain
    Neurosci Lett 461:60-4. 2009
  5. ncbi Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification
    J Ramirez
    Dept of Signal Theory, Networking and Communications, University of Granada, Periodista Daniel Saucedo Aranda S N, 18071 Granada, Spain
    Neurosci Lett 472:99-103. 2010
  6. ncbi SVM-based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting
    R Chaves
    Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
    Neurosci Lett 461:293-7. 2009
  7. ncbi Analysis of SPECT brain images for the diagnosis of Alzheimer's disease based on NMF for feature extraction
    P Padilla
    Department of Signal Theory, Networking and Communications, University of Granada, Fuentenueva s n, Granada, Spain
    Neurosci Lett 479:192-6. 2010
  8. ncbi Classification of functional brain images using a GMM-based multi-variate approach
    F Segovia
    Department of Signal Theory, Networking and Communications, University of Granada, Fuentenueva s n, Granada, Spain
    Neurosci Lett 474:58-62. 2010
  9. ncbi SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA
    M M Lopez
    Dept of Signal Theory, Networking and Communications, University of Granada, Spain
    Neurosci Lett 464:233-8. 2009
  10. ncbi NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer's disease
    P Padilla
    Department of Signal Theory, Telematics and Communications, University of Granada, 18071 Granada, Spain
    IEEE Trans Med Imaging 31:207-16. 2012

Collaborators

Detail Information

Publications12

  1. ncbi Feature selection using factor analysis for Alzheimer's diagnosis using 18F-FDG PET images
    D Salas-Gonzalez
    Department of Signal Theory, Networking and Communications, ETSIIT 18071, University of Granada, Granada, Spain
    Med Phys 37:6084-95. 2010
    ..Two hundred and ten 18F-FDG PET images from the ADNI initiative [52 normal controls (NC), 114 mild cognitive impairment (MCI), and 53 AD subjects] are studied...
  2. ncbi Parameterization of the distribution of white and grey matter in MRI using the α-stable distribution
    D Salas-Gonzalez
    Department of Signal Theory, Networking and Communications, University of Granada, Spain Electronic address
    Comput Biol Med 43:559-67. 2013
    ....
  3. ncbi Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees
    D Salas-Gonzalez
    Department of Signal Theory Networking and Communications, ETSIIT, University of Granada, Spain
    Phys Med Biol 55:2807-17. 2010
    ..They are chosen as feature vectors for two different classifiers: support vector machines with linear kernel and classification trees. The proposed methodology reaches greater than 95% accuracy in the classification task...
  4. ncbi Analysis of SPECT brain images for the diagnosis of Alzheimer's disease using moments and support vector machines
    Diego Salas-Gonzalez
    Department of Signal Theory, Networking and Communications, ETSIIT 18071, University of Granada, Spain
    Neurosci Lett 461:60-4. 2009
    ..The proposed methodology reaches accuracy higher than 99% in the classification task...
  5. ncbi Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification
    J Ramirez
    Dept of Signal Theory, Networking and Communications, University of Granada, Periodista Daniel Saucedo Aranda S N, 18071 Granada, Spain
    Neurosci Lett 472:99-103. 2010
    ..7%, and 96.9%, respectively. Moreover, the proposed CAD system outperformed several other recently developed AD CAD systems...
  6. ncbi SVM-based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting
    R Chaves
    Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
    Neurosci Lett 461:293-7. 2009
    ..3% for almost linear kernel support vector machine (SVM) defined over the 20 most discriminative features extracted. This new method outperformed recent developed methods for early AD diagnosis...
  7. ncbi Analysis of SPECT brain images for the diagnosis of Alzheimer's disease based on NMF for feature extraction
    P Padilla
    Department of Signal Theory, Networking and Communications, University of Granada, Fuentenueva s n, Granada, Spain
    Neurosci Lett 479:192-6. 2010
    ....
  8. ncbi Classification of functional brain images using a GMM-based multi-variate approach
    F Segovia
    Department of Signal Theory, Networking and Communications, University of Granada, Fuentenueva s n, Granada, Spain
    Neurosci Lett 474:58-62. 2010
    ..The leave-one-out cross-validation technique is used to validate the results obtained by the supervised learning-based computer aided diagnosis (CAD) system over databases of SPECT and PET images yielding an accuracy rate up to 96.67%...
  9. ncbi SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA
    M M Lopez
    Dept of Signal Theory, Networking and Communications, University of Granada, Spain
    Neurosci Lett 464:233-8. 2009
    ..31% accuracy rate for a SPECT database consisting of 91 patients. The proposed methodology outperforms voxels-as-features (VAF) that was considered as baseline approach, which yields 80.22% for the same SPECT database...
  10. ncbi NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer's disease
    P Padilla
    Department of Signal Theory, Telematics and Communications, University of Granada, 18071 Granada, Spain
    IEEE Trans Med Imaging 31:207-16. 2012
    ..The proposed NMF-SVM method yields up to 91% classification accuracy with high sensitivity and specificity rates (upper than 90%). This NMF-SVM CAD tool becomes an accurate method for SPECT and PET AD image classification...
  11. ncbi Efficient mining of association rules for the early diagnosis of Alzheimer's disease
    R Chaves
    Department of Signal Theory, Networking and Communication, ETSIIT, 18071, University of Granada, Spain
    Phys Med Biol 56:6047-63. 2011
    ..The proposed methods were validated by means of the leave-one-out cross validation strategy, yielding up to 94.87% classification accuracy, thus outperforming recent developed methods for computer aided diagnosis of AD...
  12. ncbi Automatic selection of ROIs in functional imaging using Gaussian mixture models
    J M Gorriz
    Departamento Teoría de la Se nal, Telemática y Comunicaciones, Universidad Granada, Spain
    Neurosci Lett 460:108-11. 2009
    ..This approach leads to a drastic compression of the information contained in the brain image and serves as a starting point for a variety of possible feature extraction methods for the diagnosis of brain diseases...