Fermín Segovia

Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer's disease

In Alzheimers disease (AD) diagnosis process, functional brain image modalities such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians. However, the current evaluation of these images entails a succession of manual reorientations and visual interpretation steps, which attach in some way subjectivity to the diagnostic. In this work, a complete computer aided diagnosis (CAD) system for an automatic evaluation of the neuroimages is presented. Principal component analysis (PCA)-based methods are proposed as feature extraction techniques, enhanced by other linear approaches such as linear discriminant analysis (LDA) or the measure of the Fisher discriminant ratio (FDR) for feature selection. The final features allow to face up the so-called small sample size problem and subsequently they are used for the study of neural networks (NN) and support vector machine (SVM) classifiers. The combination of the presented methods achieved accuracy results of up to 96.7% and 89.52% for SPECT and PET images, respectively, which means a significant improvement over the results obtained by the classical voxels-as-features (VAF) reference approach.

18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis

Finding sensitive and appropriate technologies for non-invasive observation and early detection of Alzheimers disease (AD) is of fundamental importance to develop early treatments. In this work we develop a fully automatic computer aided diagnosis (CAD) system for high-dimensional pattern classification of baseline ^1^8F-FDG PET scans from Alzheimers disease neuroimaging initiative (ADNI) participants. Image projection as feature space dimension reduction technique is combined with an eigenimage based decomposition for feature extraction, and support vector machine (SVM) is used to manage the classification task. A two folded objective is achieved by reaching relevant classification performance complemented with an image analysis support for final decision making. A 88.24% accuracy in identifying mild AD, with 88.64% specificity, and 87.70% sensitivity is obtained. This method also allows the identification of characteristic AD patterns in mild cognitive impairment (MCI) subjects.

SVM-based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting

This letter shows a computer-aided diagnosis (CAD) technique for the early detection of the Alzheimers disease (AD) based on single photon emission computed tomography (SPECT) image feature selection and a statistical learning theory classifier. The challenge of the curse of dimensionality is addressed by reducing the large dimensionality of the input data and defining normalized mean squared error features over regions of interest (ROI) that are selected by a t-test feature selection with feature correlation weighting. Thus, normalized mean square error (NMSE) features of cubic blocks located in the temporo-parietal brain region yields peak accuracy values of 98.3% 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.

SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA

Single-photon emission tomography (SPECT) imaging has been widely used to guide clinicians in the early Alzheimers disease (AD) diagnosis challenge. However, AD detection still relies on subjective steps carried out by clinicians, which entail in some way subjectivity to the final diagnosis. In this work, kernel principal component analysis (PCA) and linear discriminant analysis (LDA) are applied on functional images as dimension reduction and feature extraction techniques, which are subsequently used to train a supervised support vector machine (SVM) classifier. The complete methodology provides a kernel-based computer-aided diagnosis (CAD) system capable to distinguish AD from normal subjects with 92.31% 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.

Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification.

This letter shows a computer aided diagnosis (CAD) technique for the early detection of the Alzheimers disease (AD) by means of single photon emission computed tomography (SPECT) image classification. The proposed method is based on partial least squares (PLS) regression model and a random forest (RF) predictor. The challenge of the curse of dimensionality is addressed by reducing the large dimensionality of the input data by downscaling the SPECT images and extracting score features using PLS. A RF predictor then forms an ensemble of classification and regression tree (CART)-like classifiers being its output determined by a majority vote of the trees in the forest. A baseline principal component analysis (PCA) system is also developed for reference. The experimental results show that the combined PLS-RF system yields a generalization error that converges to a limit when increasing the number of trees in the forest. Thus, the generalization error is reduced when using PLS and depends on the strength of the individual trees in the forest and the correlation between them. Moreover, PLS feature extraction is found to be more effective for extracting discriminative information from the data than PCA yielding peak sensitivity, specificity and accuracy values of 100%, 92.7%, and 96.9%, respectively. Moreover, the proposed CAD system outperformed several other recently developed AD CAD systems.

Early diagnosis of Alzheimer׳s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images

Abstract Computer aided diagnosis (CAD) systems using functional and structural imaging techniques enable physicians to detect early stages of the Alzheimer׳s disease (AD). For this purpose, magnetic resonance imaging (MRI) have been proved to be very useful in the assessment of pathological tissues in AD. This paper presents a new CAD system that allows the early AD diagnosis using tissue-segmented brain images. The proposed methodology aims to discriminate between AD, mild cognitive impairment (MCI) and elderly normal control (NC) subjects and is based on several multivariate approaches, such as partial least squares (PLS) and principal component analysis (PCA). In this study, 188 AD patients, 401 MCI patients and 229 control subjects from the Alzheimer׳s Disease Neuroimaging Initiative (ADNI) database were studied. Automated brain tissue segmentation was performed for each image obtaining gray matter (GM) and white matter (WM) tissue distributions. The validity of the analyzed methods was tested on the ADNI database by implementing support vector machine classifiers with linear or radial basis function (RBF) kernels to distinguish between normal subjects and AD patients. The performance of our methodology is validated using k-fold cross technique where the system based on PLS feature extraction and linear SVM classifier outperformed the PCA method. In addition, PLS feature extraction is found to be more effective for extracting discriminative information from the data. In this regard, the developed latter CAD system yielded maximum sensitivity, specificity and accuracy values of 85.11%, 91.27% and 88.49%, respectively.

GMM based SPECT image classification for the diagnosis of Alzheimer's disease

We present a novel classification method of SPECT images based on Gaussian mixture models (GMM) for the diagnosis of Alzheimers disease. The aims of the model-based approach for density estimation is to automatically select regions of interest (ROIs) and to effectively reduce the dimensionality of the problem. The resulting Gaussians are constructed according to a maximum likelihood criterion employing the Expectation Maximization (EM) algorithm. By considering only the intensity levels inside the Gaussians, the resulting feature space has a significantly reduced dimensionality with respect to former approaches using the voxel intensities directly as features (VAF). With this feature extraction method one relieves the effects of the so-called small sample size problem and nonlinear classifiers may be used to distinguish between the brain images of normal and Alzheimer patients. Our results show that for various classifiers the GMM-based method yields higher accuracy rates than the classification considering all voxel values.

Automatic assistance to Parkinson's disease diagnosis in DaTSCAN SPECT imaging.

PURPOSE In this work, an approach to computer aided diagnosis (CAD) system is proposed as a decision-making aid in Parkinsonian syndrome (PS) detection. This tool, intended for physicians, entails fully automatic preprocessing, normalization, and classification procedures for brain single-photon emission computed tomography images. METHODS Ioflupane[(123)I]FP-CIT images are used to provide in vivo information of the dopamine transporter density. These images are preprocessed using an automated template-based registration followed by two proposed approaches for intensity normalization. A support vector machine (SVM) is used and compared to other statistical classifiers in order to achieve an effective diagnosis using whole brain images in combination with voxel selection masks. RESULTS The CAD system is evaluated using a database consisting of 208 DaTSCAN images (100 controls, 108 PS). SVM-based classification is the most efficient choice when masked brain images are used. The generalization performance is estimated to be 89.02 (90.41-87.62)% sensitivity and 93.21 (92.24-94.18)% specificity. The area under the curve can take values of 0.9681 (0.9641-0.9722) when the image intensity is normalized to a maximum value, as derived from the receiver operating characteristics curves. CONCLUSIONS The present analysis allows to evaluate the impact of the design elements for the development of a CAD-system when all the information encoded in the scans is considered. In this way, the proposed CAD-system shows interesting properties for clinical use, such as being fast, automatic, and robust.

A comparative study of feature extraction methods for the diagnosis of Alzheimer's disease using the ADNI database

Several approaches appear in literature in order to develop Computed-Aided-Diagnosis (CAD) systems for Alzheimers disease (AD) detection. Although univariate models became very popular and nowadays they are widely used, recent investigations are focused on multivariate models which deal with a whole image as an observation. In this work, we compare two multivariate approaches that use different methodologies to relieve the small sample size problem. One of them is based on Gaussian Mixture Model (GMM) and models the Regions of Interests (ROIs) defined as differences between controls and AD subject. After GMM estimation using the EM algorithm, feature vectors are extracted for each image depending on the positions of the resulting Gaussians. The other method under study computes score vectors through a Partial Least Squares (PLS) algorithm based estimation and those vectors are used as features. Before extracting the score vectors, a binary mask based dimensional reduction of the input space is performed in order to remove low-intensity voxels. The validity of both methods is tested on the ADNI database by implementing several CAD systems with linear and nonlinear classifiers and comparing them with previous approaches such as VAF and PCA.

Computer aided diagnosis of Alzheimer's disease using component based SVM

Abstract: Alzheimers disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioural impairments and eventually causing death. Functional brain imaging as single-photon emission computed tomography (SPECT) is commonly used to guide the clinicians diagnosis. However, conventional evaluation of these scans often relies on manual reorientation, visual reading and semi-quantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on a first automatic feature selection, and secondly a combination of component-based support vector machine (SVM) classification and a pasting votes technique of assembling SVM classifiers.