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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.

Electronically Reconfigurable Transmitarray at Ku Band for Microwave Applications

An electronically reconfigurable transmitarray device at 12 GHz is presented in this work. This paper highlights the functioning of this kind of device and thoroughly examines the proposed reconfigurable transmitarray. The architecture is discussed along with the design and selection of all the constituting elements and the prototypes for all of them. In order to add reconfigurability to the transmitarray structure, 360° reflective phase shifters were designed, prototyped and validated for direct application. Eventually, a demonstrative prototype for an active transmitarray with phase shifters was assembled, and radiation pattern measurements were taken in an anechoic chamber to demonstrate the capabilities of this structure.

NMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer's Disease

This paper presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimers disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two different brain image databases are selected: a single photon emission computed tomography (SPECT) database and positron emission tomography (PET) images, both of them containing data for both Alzheimers disease (AD) patients and healthy controls as a reference. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and nonnegative matrix factorization (NMF) for feature selection and extraction of the most relevant features. The resulting NMF-transformed sets of data, which contain a reduced number of features, are classified by means of a SVM-based classifier with bounds of confidence for decision. The proposed NMF-SVM method yields up to 91% classification accuracy with high sensitivity and specificity rates (upper than 90%). This NMF-SVM CAD tool becomes an accurate method for SPECT and PET AD image classification.

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.

Feature selection using factor analysis for Alzheimer's diagnosis using 18F-FDG PET images.

PURPOSE This article presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of Alzheimers disease (AD). Two hundred and tenF18-FDG PET images from the ADNI initiative [52 normal controls (NC), 114 mild cognitive impairment (MCI), and 53 AD subjects] are studied. METHODS The proposed methodology is based on the selection of voxels of interest using the t-test and a posterior reduction of the feature dimension using factor analysis. Factor loadings are used as features for three different classifiers: Two multivariate Gaussian mixture model, with linear and quadratic discriminant function, and a support vector machine with linear kernel. RESULTS An accuracy rate up to 95% when NC and AD are considered and an accuracy rate up to 88% and 86% for NC-MCI and NC-MCI, AD, respectively, are obtained using SVM with linear kernel. CONCLUSIONS Results are compared to the voxel-as-features and a PCA- based approach and the proposed methodology achieves better classification performance.

Projecting independent components of SPECT images for computer aided diagnosis of Alzheimer's disease

Finding sensitive and appropriate technologies for early detection of the Alzheimers disease (AD) are of fundamental importance to develop early treatments. Single Photon Emission Computed Tomography (SPECT) images are non-invasive observation tools to assist the diagnosis, commonly processed through unsupervised statistical tests, or assessed visually. In this work, we present a computer aided diagnosis system based on supervised learning methods, exploring two different novel approaches. Independent Component Analysis (ICA) was used within this work to extract the relevant features from the image database and reduce the feature space dimensionality, to build a SVM with the resulting data. The proposed approach led to an error estimation below the 9%, and was able to detect the AD perfusion pattern and classify new subjects in an unsupervised manner.

Classification of functional brain images using a GMM-based multi-variate approach

This paper presents a novel method for automatic selection of regions of interest (ROIs) of functional brain images based on Gaussian mixture models (GMM), which relieves the so-called small size sample problem in the classification of functional brain images for the diagnosis of Alzheimers disease (AD). In a first step, brain images are preprocessed in order to find an average image including differences between controls and AD patients. Then, ROIs are extracted using a GMM which is adjusted by using the expectation maximization (EM) algorithm. This reduced set of features provides the activation map of each patient and allows us to train statistical classifiers based on support vector machines (SVMs). The leave-one-out cross-validation technique is used to validate the results obtained by the supervised learning-based computer aided diagnosis (CAD) system over databases of SPECT and PET images yielding an accuracy rate up to 96.67%.

Mutual Coupling Reduction Using EBG in Steering Antennas

In this letter, a dual circular polarized steering antenna for satellite communications in X-band is presented. This antenna consists of printed elements grouped in an array, able to work from 7.25 up to 8.4 GHz in both polarizations: left-handed circular polarization (LHCP) and right-handed circular polarization (RHCP). The module antenna is compact, with narrow beamwidth, and reaches a gain of 16 dBi. It has the capability to steer in elevation to ±10° and ±40° electronically with a Butler matrix. In order to reduce the mutual coupling between adjacent patches, electromagnetic band-gap (EBG) structures are introduced. These EBGs combine double-layer and edge location via in order to reduce the size, without changing the low-permittivity substrate, and therefore maintaining the high radiation efficiency of the antenna.

Regions of interest computed by SVM wrapped method for Alzheimer's disease examination from segmented MRI.

Accurate identification of the most relevant brain regions linked to Alzheimer’s disease (AD) is crucial in order to improve diagnosis techniques and to better understand this neurodegenerative process. For this purpose, statistical classification is suitable. In this work, a novel method based on support vector machine recursive feature elimination (SVM-RFE) is proposed to be applied on segmented brain MRI for detecting the most discriminant AD regions of interest (ROIs). The analyses are performed both on gray and white matter tissues, achieving up to 100% accuracy after classification and outperforming the results obtained by the standard t-test feature selection. The present method, applied on different subject sets, permits automatically determining high-resolution areas surrounding the hippocampal area without needing to divide the brain images according to any common template.

Erratum to: On the Influence of the Propagation Channel in the Performance of Energy-Efficient Geographic Routing Algorithms for Wireless Sensor Networks (WSN)

In this paper, the influence of the features of the propagation channel in the performance of energy-efficient routing algorithms for wireless sensor networks is studied. Although there are a lot of works regarding energy-efficient routing protocols, almost no reference to realistic propagation channel models and influence is made in the literature. Considering that the propagation channel may affect the efficiency of the different energy-efficient routing algorithms, different propagation scenarios are proposed in this work, from the most simplistic free-space propagation model to more complex ones. The latter includes the effects of multipath propagation, shadowing, fading, etc. In addition, spatial diversity transmission/reception models are considered to mitigate the effects of hard propagation fading. Some results are provided comparing the performance of several energy-efficient routing algorithms in different scenarios.