Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.


Autoria(s): Chaves, Rosa; Ramírez, Javier; Górriz, Juan M; Illán, Ignacio A; Gómez-Río, Manuel; Carnero, Cristobal
Data(s)

25/11/2013

25/11/2013

31/07/2012

Resumo

BACKGROUND Functional 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. METHODS It 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. RESULTS Several 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. CONCLUSIONS All 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).

Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't;

This work was partly supported by the MICINN of Spain under the TEC2008-02113 and TEC2012-34306 project and the Consejeria de Innovacion, Ciencia y Empresa (Junta de Andalucia, Spain) under the Excellence Projects P07-TIC-02566, P09-TIC- 4530 and P11-TIC-7103. The PET data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, Amorfix Life Sciences Ltd., AstraZeneca, Bayer HealthCare; BioClinica, Inc., Biogen Idec Inc., Bristol-Myers Squibb Company, Eisai Inc., Elan Pharmaceuticals Inc., Eli Lilly and Company, F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc., GE Healthcare, Innogenetics, N.V., IXICO Ltd., Janssen Alzheimer Immunotherapy Research and Development, LLC., Johnson and Johnson Pharmaceutical Research and Development LLC., Medpace, Inc., Merck and Pfizer Inc., Servier, Synarc Inc., and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 and K01 AG030514.

Identificador

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

1472-6947 (Online)

PMC3512495

http://hdl.handle.net/10668/1395

22849649

10.1186/1472-6947-12-79

Idioma(s)

en

Publicador

BioMed Central

Relação

BMC medical informatics and decision making

http://www.biomedcentral.com/1472-6947/12/79/abstract

Direitos

Acceso abierto

Palavras-Chave #Anciano #Algoritmos #Enfermedad de Alzheimer #Inteligencia Artificial #Interpretación Estadística de Datos #Diagnóstico Precoz #Femenino #Neuroimagen Funcional #Interpretación de Imagen Asistida por Computador #Tomografía de Emisión de Positrones #Análisis de Componente Principal #Radiofármacos #Sensibilidad y Especificidad #España #Tomografía Computarizada de Emisión de Fotón Único #Medical Subject Headings::Named Groups::Persons::Age Groups::Adult::Aged #Medical Subject Headings::Named Groups::Persons::Age Groups::Adult::Aged::Aged, 80 and over #Medical Subject Headings::Phenomena and Processes::Mathematical Concepts::Algorithms #Medical Subject Headings::Diseases::Nervous System Diseases::Central Nervous System Diseases::Brain Diseases::Dementia::Alzheimer Disease #Medical Subject Headings::Information Science::Information Science::Computing Methodologies::Artificial Intelligence #Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Decision Support Techniques::Data Interpretation, Statistical #Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Early Diagnosis #Medical Subject Headings::Check Tags::Female #Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Neuroimaging::Functional Neuroimaging #Medical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans #Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Diagnosis, Computer-Assisted::Image Interpretation, Computer-Assisted #Medical Subject Headings::Check Tags::Female #Medical Subject Headings::Named Groups::Persons::Age Groups::Adult::Middle Aged #Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Image Interpretation, Computer-Assisted::Tomography, Emission-Computed::Positron-Emission Tomography #Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Principal Component Analysis #Medical Subject Headings::Chemicals and Drugs::Chemical Actions and Uses::Specialty Uses of Chemicals::Laboratory Chemicals::Indicators and Reagents::Radiopharmaceuticals #Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Sensitivity and Specificity #Medical Subject Headings::Geographicals::Geographic Locations::Europe::Spain #Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Image Interpretation, Computer-Assisted::Tomography, Emission-Computed::Tomography, Emission-Computed, Single-Photon #Medical Subject Headings::Named Groups::Persons::Age Groups::Adult
Tipo

info:eu-repo/semantics/article

info:eu-repo/semantics/published

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