RT Journal Article
T1 Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.
A1 Chaves, Rosa
A1 Ramírez, Javier
A1 Górriz, Juan M
A1 Illán, Ignacio A
A1 Gómez-Río, Manuel
A1 Carnero, Cristobal
K1 Anciano
K1 Algoritmos
K1 Enfermedad de Alzheimer
K1 Inteligencia Artificial
K1 Interpretación Estadística de Datos
K1 Diagnóstico Precoz
K1 Femenino
K1 Neuroimagen Funcional
K1 Interpretación de Imagen Asistida por Computador
K1 Tomografía de Emisión de Positrones
K1 Análisis de Componente Principal
K1 Radiofármacos
K1 Sensibilidad y Especificidad
K1 España
K1 Tomografía Computarizada de Emisión de Fotón Único
AB BACKGROUNDFunctional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems.METHODSIt is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared.RESULTSSeveral experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods.CONCLUSIONSAll the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).
PB BioMed Central
YR 2012
FD 2012-07-31
LK http://hdl.handle.net/10668/1395
UL http://hdl.handle.net/10668/1395
LA en
NO Chaves R, Ramírez J, Górriz JM, Illán IA, Gómez-Río M, Carnero C. Effective diagnosis of Alzheimer's disease by means of large margin-based methodology. BMC Med Inform Decis Mak; 12:79
NO Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't;
DS RISalud
RD Apr 5, 2025