Abdelbasset Brahim

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.

Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer’s with Visual Support

Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimers disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimers disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making. The CAD is built in two stages: a first feature extraction based on independent component analysis (ICA) on class mean images and, secondly, a support vector machine (SVM) training and classification. The obtained features for classification offer a full graphical representation of the images, giving an understandable logic in the CAD output, that can increase confidence in the CAD support. The proposed method yields classification results up to 89% of accuracy (with 92% of sensitivity and 86% of specificity) for normal controls (NC) and AD patients, 79% of accuracy (with 82% of sensitivity and 76% of specificity) for NC and mild cognitive impairment (MCI), and 85% of accuracy (with 85% of sensitivity and 86% of specificity) for MCI and AD patients.

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.

Independent Component Analysis-Based Classification of Alzheimer’s Disease from Segmented MRI Data

An accurate and early diagnosis of the Alzheimer’s disease (AD) is of fundamental importance to improve diagnosis techniques, to better understand this neurodegenerative process and to develop effective treatments. In this work, a novel classification method based on independent component analysis (ICA) and supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer’s disease neuroimaging initiative (ADNI) participants for automatic classification task. The ICA-based method is composed of three step. First, MRI are normalized and segmented by the Statistical Parametric Mapping (SPM8) software. After that, average image of normal (NC), mild cognitive impairment (MCI) or AD subjects are computed. Then, FastICA is applied to these different average images for extracting a set of independent components (IC) which symbolized each class characteristics. Finally, each brain image from the database was projected onto the space spanned by this independent components basis for feature extraction, a support vector machine (SVM) is used to manage the classification task. A 87.5% accuracy in identifying AD from NC, with 90.4% specificity and 84.6% sensitivity is obtained. According to the experimental results, we can see that this proposed method can successfully differentiate AD, MCI and NC subjects. So, it is suitable for automatic classification of sMRI images.

Applications of Gaussian mixture models and mean squared error within DatSCAN SPECT imaging

This work highlights the exploitation of Gaussian Mixture Model (GMM) and Mean squared Error (MSE) in DaTSCAN SPECT brain images for intensity normalization purposes over two proposed approaches. The first proposed methodology is based on a nonlinear image filtering by means of GMM, which considers not only the intensity levels of each voxel but also its coordinates inside the so-defined spatial Gaussian functions. It is achieved according to a probability threshold that measures the weight of each kernel or cluster on the striatum area, the voxels in the non-specific regions are intensity normalized by removing clusters whose likelihood is negligible. The second normalization method based on MSE which is performed by a linear intensity transformation in each voxel. This approach is based on predicting jointly different intensity normalization parameters that leads to the joint minimization of the squared sum errors between the template image and the optimal linear estimated image (normalized image). We compare these methods of normalization together with another approach widely used based on specific-to-non-specific binding ratio. This comparison is based on DaTSCAN image analysis and classification for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome detection.

A proposed computer-aided diagnosis system for Parkinson's disease classification using 123 I-FP-CIT imaging

This paper presents a fully automatic computer aided diagnosis (CAD) system for the classification of Parkinsons disease (PD) by means of functional imaging, such as, the single photon emission computed tomography (SPECT). Firstly, in the preprocessing step, Histogram Equalization (HE) is applied on all the 3D SPECT image data. Secondly, HE is applied on the so-called non-specific (NS) region, as reference region. Then, the normalized images are modelled using Principal Component Analysis (PCA). Thus, for each subject, its scan is represented by a few components. These resulting features will be used for the classification task. The proposed system has been tested on a 269 image database from the Parkinson Progression Markers Initiative (PPMI). Classification rate of 92.63% is achieved, which has proved the robustness and the productiveness of the proposed CAD system in PD pattern detection. In addition, the PCA based feature extraction approach significantly improves the baseline Voxels-as-Features (VAF) method, used as an approximation of the visual analysis. Finally, the proposed aided diagnosis system outperforms several other recently developed PD CAD systems.

Intensity Normalization of 123 I-ioflupane-SPECT Brain Images Using a Model-Based Multivariate Linear Regression Approach

The intensity normalization step is essential, as it corresponds to the initial step in any subsequent computer-based analysis. In this work, a proposed intensity normalization approach based on a predictive modeling using multivariate linear regression (MLR) is presented. Different intensity normalization parameters derived from this model will be used in a linear procedure to perform the intensity normalization of 123 I-ioflupane-SPECT brain images. This proposed approach is compared to conventional intensity normalization methods, such as specific-to-non-specific binding ratio, integral-based intensity normalization and intensity normalization by minimizing the Kullback-Leibler divergence. For the performance evaluation, a statistical analysis is used by applying the Euclidean distance and the Jeffreys divergence. In addition, a classification task using support vector machine to evaluate the impact of the proposed methodology for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome detection.