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

BMC Medical Informatics and Decision Making

Research Article

Cristobal Carnero¹  Manuel Gómez-Río²  Javier Ramírez³  Rosa Chaves³  Juan M Górriz³  Ignacio A Illán³ 

[1] Department of Neurology, University Hospital Virgen de las Nieves, Granada, Spain;Department of Nuclear Medicine, University Hospital Virgen de las Nieves, Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, c/Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain;

关键词: Partial Little Square;    Normalize Mean Square Error;    Kernel Principal Component Analysis;    Small Sample Size Problem;    Target Neighbour;   

DOI  :  10.1186/1472-6947-12-79

 received in 2011-05-13, accepted in 2012-06-27,  发布年份 2012

来源: Springer

【 摘 要 】

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).

【 授权许可】

CC BY   
© Chaves et al.; licensee BioMed Central Ltd. 2012