Abdelbasset Brahim

Abdelbasset Brahim
University of Granada | UGR · Department of Signal Theory, Telematics and Communications

PhD, Msc, Bsc

About

22
Publications
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464
Citations

Publications

Publications (22)
Article
Full-text available
The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent...
Preprint
Full-text available
The application of graph theory to model the complex structure and function of the brain has shed new light on its organization and function, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain acti...
Conference Paper
Full-text available
La transformée de Fourier sur graphe (TFG) pourrait être un outil essentiel pour l'analyse des signaux cérébraux. En ce sens, nous évaluons l'application du traitement de signal sur graphes (TSG) pour l'analyse des données de neuroimagerie. Ainsi, une approche basée sur le TSG est proposée et validée pour la classification des troubles du spectre a...
Poster
Graph Fourier Transform (GFT) could be a key tool for analyzing brain signals (Huang et al. 2018). In this work, we evaluate the application of Graph signal processing (GSP) for the analysis of neuroimaging data. More specifically, we characterize each fMRI time series of each brain subject by its standard deviation (STD). Then, we project these st...
Chapter
Graph Fourier Transform (GFT) could be a key tool for analyzing brain signals. In this sense, we evaluate the application of Graph signal processing (GSP) for the analysis of neuroimaging data. Thus, a GSP-based approach is proposed and validated for the classification of autism spectrum disorder (ASD). More specifically, the resting state function...
Chapter
Full-text available
In this paper, an aided diagnosis method for OsteoArthritis (OA) disease using knee X-ray imaging and spectral analysis is presented. The proposed method is based on the Power Spectral Density (PSD) over different orientations of the image as a feature for the classification task. Then, independent component analysis (ICA) is used to select the rel...
Article
This paper presents a fully developed computer aided diagnosis (CAD) system for early knee OsteoArthritis (OA) detection using knee X-ray imaging and machine learning algorithms. The X-ray images are first preprocessed in the Fourier domain using a circular Fourier filter. Then, a novel normalization method based on predictive modeling using multiv...
Article
Full-text available
Abstract The classification of subjects with different stages of knee OsteoArthritis (OA) using bone texture analysis is a challenging task in medical imaging. This paper presents a new approach for texture analysis of radiographic OA in knee X-ray images. First, a preprocessing step based on a 2D finite impulse response filter is applied on the X-...
Article
Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's 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 Alzheimer's d...
Article
This paper presents a novel method for intensity normalization of DaTSCAN SPECT brain images. The proposed methodology is based on Gaussian mixture models (GMMs) and considers not only the intensity levels, but also the coordinates of voxels inside the so-defined spatial Gaussian functions. The model parameters are obtained according to a maximum l...
Article
Full-text available
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 w...
Conference Paper
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...
Conference Paper
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 t...
Article
Full-text available
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 th...
Article
Full-text available
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 w...
Article
The analysis of 3D SPECT brain images requires several pre-processing steps such as intensity normalization and brain feature extraction. In this sense, a new method for intensity normalization of <sup>123</sup>I-ioflupane-SPECT (DaTSCAN) brain images based on minimization of the Mean Square Error (MSE) between the Gaussian Mixture Model (GMM)-base...
Conference Paper
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 al...
Conference Paper
Full-text available
This paper proposes a novel method for automatic classification of mag-netic resonance images (MRI) based on independent component analysis (ICA). Our methodology consists of three processing steps. First, all the MRI scans are normalized and segmented into gray matter, white matter and cerebrospinal fluid. Then, ICA is applied to the preprocessed...
Conference Paper
Full-text available
In this paper, we present 3D FEM simulations of a CMOS MEMS convective accelerometer. Differences between 3D and previously published 2D simulations are also discussed. We describe sensor architecture and we present a sensor model that is used for 3D FEM simulations. The prototype has a heater-cavity border distance of 350μm. We show the details of...

Projects

Project (1)
Project
The LAGRANGE project tries to afford the process of improving the sanitary assistance and the wellfare of the citizens by means of the development of automatic tools and systems enabling the early diagnosis and the treatment of the most prevalent neurodegenerative diseases that, in agreement with the WHO reports, are the Alzheimer's disease (EA) and the Parkinson's disease (EP), over which the progressive aging of the population is going to have more incidence in the next decades. The recent update of the NIA related to the criteria and directives for diagnosis, which incorporates the use of new biomarkers, that is to say, biological measurable factors that could indicate the presence or absence of disease, or the risk of developing it, motivates the development, optimization and validation medical brain images preprocessing and analysis methods, including structural magnetic resonance and functional tomography: PET and SPECT, so that these techniques can be used in clinical environments. With this approach, the project LAGRANGE takes as a principal aim the development of new methods based on IT (Information Technologies) for the treatment, analysis and classification of medical brain images of structural and functional modalities, for the development of aid systems in the early diagnosis and prediction of the risk of developing the EA and the EP. In addition, It is tried to reach a step beyond, towards the evaluation of these techniques in clinical environments facilitating their use to third parties by means of the integration of the contributions of this project in a WEB platform and mobile applications for automatic analysis of medical brain images. The team will actively participate in two multi-lab international studies: ADNI (Alzheimer's Disease Neuroimaging Initivative) and PPMI (Parkinson's Progression Markers Initiative), being the fundamental aim of these initiatives the study of biomarkers that describe the progression of the EA and the EP, respectively. Across these initiatives there will be access to a wide collection of biomarkers that offer new opportunities for the development of computational methods that could have a strong impact in the study, diagnosis, prognosis, development of therapeutic strategies and follow-up of the patients affected by these neurodegenerative diseases. Among the contributions of the project, it is expected to advance in the design of strategies of selection, extraction and classification of multimodal information and in the development of models that describe the temporary evolution of the patient, including capacity of prediction of the short or long term progression of the disease. The LAGRANGE project will be developed in a collaborative environment, with the participation of several international centers of recognized prestige. In addition, the project owns the support of several technological companies and entities that have strong interest in the possible exploitation of the results of the project, offering technology transfer opportunities.