Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data.

Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/ or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data. As shown by our results, the CAD proposal is able to detect PD with [Formula: see text] of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.

Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.

BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10-15% per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments. METHOD: The proposed system, developed by the SiPBA-UGR team for this challenge, is based on feature standardization, ANOVA feature selection, partial least squares feature dimension reduction and an ensemble of One vs. Rest random forest classifiers. With the aim of improving its performance when discriminating healthy controls (HC) from MCI, a second binary classification level was introduced that reconsiders the HC and MCI predictions of the first level. RESULTS: The system was trained and evaluated on an ADNI datasets that consist of T1-weighted MRI morphological measurements from HC, stable MCI, converter MCI and AD subjects. The proposed system yields a 56.25% classification score on the test subset which consists of 160 real subjects. COMPARISON WITH EXISTING METHOD(S): The classifier yielded the best performance when compared to: (i) One vs. One (OvO), One vs. Rest (OvR) and error correcting output codes (ECOC) as strategies for reducing the multiclass classification task to multiple binary classification problems, (ii) support vector machines, gradient boosting classifier and random forest as base binary classifiers, and (iii) bagging ensemble learning. CONCLUSIONS: A robust method has been proposed for the international challenge on MCI prediction based on MRI data. The system yielded the second best performance during the competition with an accuracy rate of 56.25% when evaluated on the real subjects of the test set.

On the use of multi-dimensional scaling and electromagnetic tracking in high dose rate brachytherapy.

High dose rate brachytherapy affords a frequent reassurance of the precise dwell positions of the radiation source. The current investigation proposes a multi-dimensional scaling transformation of both data sets to estimate dwell positions without any external reference. Furthermore, the related distributions of dwell positions are characterized by uni-or bi-modal heavy-tailed distributions. The latter are well represented by α-stable distributions. The newly proposed data analysis provides dwell position deviations with high accuracy, and, furthermore, offers a convenient visualization of the actual shapes of the catheters which guide the radiation source during the treatment.

An Optimal Approach for Selecting Discriminant Regions for the Diagnosis of Alzheimer's Disease.

In this work, we present a fully automatic computer-aided diagnosis method for the early diagnosis of the Alzheimer's disease. We study the distance between classes (labelled as normal controls and possible Alzheimer's disease) calculated in 116 regions of the brain using the Welchs's t-test. We select the regions with highest Welchs's t-test value as features to perform classification. Furthermore, we also study the less discriminative region according to the t-test (regions with lowest t-test absolute values) in order to use them as reference. We show that the mean and standard deviation of the intensity values in these two regions, the less and most discriminative according to the Welch's ttest, can be combined as a vector. The modulus and phase of this vector reveal statistical differences between groups which can be used to improve the classification task. We show how they can be used as input for a support vector machine classifier. The proposed methodology is tested in a SPECT brain database of 70 SPECT brain images yielding an accuracy up to 91.5% for a wide range of selected voxels.

Functional Biomedical Images of Alzheimer's Disease. A Green's Function-based Empirical Mode Decomposition Study.

Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D-EMD) provides means to analyze such images. It decomposes the latter into characteristic modes which represent textures on different spatial scales. These textures provide informative features for subsequent classification purposes. The study proposes a new EMD variant which relies on a Green's function based estimation method including a tension parameter to fast and reliably estimate the envelope hypersurfaces interpolating extremal points of the two-dimensional intensity distrubution of the images. The new method represents a fast and stable bi-dimensional EMD which speeds up computations roughly 100-fold. In combination with proper classifiers these exploratory feature extraction techniques can form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from Alzheimer's disease are taken to illustrate this ability.

Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism.

Intensity normalization is an important pre-processing step in the study and analysis of DaTSCAN SPECT imaging. As most automatic supervised image segmentation and classification methods base their assumptions regarding the intensity distributions on a standardized intensity range, intensity normalization takes on a very significant role. In this work, a comparison between different novel intensity normalization methods is presented. These proposed methodologies are based on Gaussian Mixture Model (GMM) image filtering and mean-squared error (MSE) optimization. The GMM-based image filtering method is achieved according to a probability threshold that removes the clusters whose likelihood are negligible in the non-specific regions. The MSE optimization method consists of a linear transformation that is obtained by minimizing the MSE in the non-specific region between the intensity normalized image and the template. The proposed intensity normalization methods are compared to: i) a standard approach based on the specific-to-non-specific binding ratio that is widely used, and ii) a linear approach based on the α-stable distribution. This comparison is performed on a DaTSCAN image database comprising analysis and classification stages for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome (PS) detection. In addition, these proposed methods correct spatially varying artifacts that modulate the intensity of the images. Finally, using the leave-one-out cross-validation technique over these two approaches, the system achieves results up to a 92.91% of accuracy, 94.64% of sensitivity and 92.65 % of specificity, outperforming previous approaches based on a standard and a linear approach, which are used as a reference. The use of advanced intensity normalization techniques, such as the GMM-based image filtering and the MSE optimization improves the diagnosis of PS.

Building a FP-CIT SPECT Brain Template Using a Posterization Approach.

Spatial affine registration of brain images to a common template is usually performed as a preprocessing step in intersubject and intrasubject comparison studies, computer-aided diagnosis, region of interest selection and brain segmentation in tomography. Nevertheless, it is not straightforward to build a template of [123I]FP-CIT SPECT brain images because they exhibit very low intensity values outside the striatum. In this work, we present a procedure to automatically build a [123I]FP-CIT SPECT template in the standard Montreal Neurological Institute (MNI) space. The proposed methodology consists of a head voxel selection using the Otsu's method, followed by a posterization of the source images to three different levels: background, head, and striatum. Analogously, we also design a posterized version of a brain image in the MNI space; subsequently, we perform a spatial affine registration of the posterized source images to this image. The intensity of the transformed images is normalized linearly, assuming that the histogram of the intensity values follows an alpha-stable distribution. Lastly, we build the [123I]FP-CIT SPECT template by means of the transformed and normalized images. The proposed methodology is a fully automatic procedure that has been shown to work accurately even when a high-resolution magnetic resonance image for each subject is not available.

Improving the convergence rate in affine registration of PET and SPECT brain images using histogram equalization.

A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences between the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as we show in this work using SPECT and PET brain images.

Parameterization of the distribution of white and grey matter in MRI using the α-stable distribution.

This work presents a study of the distribution of the grey matter (GM) and white matter (WM) in brain magnetic resonance imaging (MRI). The distribution of GM and WM is characterized using a mixture of α-stable distributions. A Bayesian α-stable mixture model for histogram data is presented and unknown parameters are sampled using the Metropolis-Hastings algorithm. The proposed methodology is tested in 18 real images from the MRI brain segmentation repository. The GM and WM distributions are accurately estimated. The α-stable distribution mixture model presented in this paper can be used as previous step in more complex MRI segmentation procedures using spatial information. Furthermore, due to the fact that the α-stable distribution is a generalization of the Gaussian distribution, the proposed methodology can be applied instead of the Gaussian mixture model, which is widely used in segmentation of brain MRI in the literature.

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 Alzheimer's 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 Alzheimer's 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.

Efficient mining of association rules for the early diagnosis of Alzheimer's disease.

In this paper, a novel technique based on association rules (ARs) is presented in order to find relations among activated brain areas in single photon emission computed tomography (SPECT) imaging. In this sense, the aim of this work is to discover associations among attributes which characterize the perfusion patterns of normal subjects and to make use of them for the early diagnosis of Alzheimer's disease (AD). Firstly, voxel-as-feature-based activation estimation methods are used to find the tridimensional activated brain regions of interest (ROIs) for each patient. These ROIs serve as input to secondly mine ARs with a minimum support and confidence among activation blocks by using a set of controls. In this context, support and confidence measures are related to the proportion of functional areas which are singularly and mutually activated across the brain. Finally, we perform image classification by comparing the number of ARs verified by each subject under test to a given threshold that depends on the number of previously mined rules. Several classification experiments were carried out in order to evaluate the proposed methods using a SPECT database that consists of 41 controls (NOR) and 56 AD patients labeled by trained physicians. The proposed methods were validated by means of the leave-one-out cross validation strategy, yielding up to 94.87% classification accuracy, thus outperforming recent developed methods for computer aided diagnosis of AD.

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 Alzheimer's disease (AD). Two hundred and ten 18F-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.

Analysis of SPECT brain images for the diagnosis of Alzheimer's disease based on NMF for feature extraction.

This letter presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of Alzheimer's disease (AD) based on non-negative matrix factorization (NMF) analysis applied to single photon emission computed tomography (SPECT) images. A baseline normalized SPECT database containing normalized data for both AD patients and healthy reference patients is selected for this study. The SPECT database is analyzed by applying the Fisher discriminant ratio (FDR) for feature selection and NMF for feature extraction of relevant components of each subject. The main goal of these preprocessing steps is to reduce the large dimensionality of the input data and to relieve the so called "curse of dimensionality" problem. The resulting NMF-transformed set of data, which contains a reduced number of features, is classified by means of a support vector machines based classification technique (SVM). The proposed NMF + SVM method yields up to 94% classification accuracy, with high sensitivity and specificity values (upper than 90%), becoming an accurate method for SPECT image classification. For the sake of completeness, comparison between another recently developed principal component analysis (PCA) plus SVM method and the proposed method is also provided, yielding results for the NMF + SVM approach that outperform the behavior of the reference PCA + SVM or conventional voxel-as-feature (VAF) plus SVM methods.

Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees.

This paper presents a computer-aided diagnosis technique for improving the accuracy of early diagnosis of Alzheimer-type dementia. The proposed methodology is based on the selection of voxels which present Welch's t-test between both classes, normal and Alzheimer images, greater than a given threshold. The mean and standard deviation of intensity values are calculated for selected voxels. They are chosen as feature vectors for two different classifiers: support vector machines with linear kernel and classification trees. The proposed methodology reaches greater than 95% accuracy in the classification task.

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 Alzheimer's 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%.

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 Alzheimer's 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.

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 Alzheimer's 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.

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 Alzheimer's 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.

Automatic selection of ROIs in functional imaging using Gaussian mixture models.

We present an automatic method for selecting regions of interest (ROIs) of the information contained in three-dimensional functional brain images using Gaussian mixture models (GMMs), where each Gaussian incorporates a contiguous brain region with similar activation. The novelty of the approach is based on approximating the grey-level distribution of a brain image by a sum of Gaussian functions, whose parameters are determined by a maximum likelihood criterion via the expectation maximization (EM) algorithm. Each Gaussian or cluster is represented by a multivariate Gaussian function with a center coordinate and a certain shape. This approach leads to a drastic compression of the information contained in the brain image and serves as a starting point for a variety of possible feature extraction methods for the diagnosis of brain diseases.