Abstract
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).
Highlights
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
In this work we propose and compare several feature extraction methods that includes on the one hand the combination of Normalised Mean Square Error (NMSE) with Principal Component Analysis (PCA) (see section Large Margin Nearest Neighbors (LMNN)) or PLS (see section Partial Least Squares (PLS)) plus the LMNN transformation
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
Summary
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. Functional brain imaging Single Positron Emission Computed Tomography (SPECT) is a widely used technique to study the functional properties of the brain [3]. After the reconstruction and a proper normalization of the SPECT raw data, taken with Tc-99m ethyl cysteinate dimer (ECD) as a tracer, one obtains an activation map displaying the local intensity of the regional cerebral blood flow (rCBF). This technique is applicable for the diagnosis of neuro-degenerative diseases like AD [4,5]. SPECT modality has lower resolution and higher variability than PET, but the use of SPECT tracers [7] is relatively cheap, and the longer half-lives when compared to PET tracers makes SPECT well suited, if not required, when biologically active radiopharmaceuticals have slow kinetics
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