Mohamed Ben Slima

Three-dimensional neuronal brain activity estimation using shrinking smooth weighted-minimum-norm focal underdetermined-system solver methods

A new nonparametric method, based on the smooth weighted-minimum-norm (WMN) focal underdetermined-system solver (FOCUSS), for electrical cerebral activity localization using electroencephalography measurements is proposed. This method iteratively adjusts the spatial sources by reducing the size of the lead-field and the weighting matrix. Thus, an enhancement of source localization is obtained, as well as a reduction of the computational complexity. The performance of the proposed method, in terms of localization errors, robustness, and computation time, is compared with the WMN-FOCUSS and nonshrinking smooth WMN-FOCUSS methods as well as with standard generalized inverse methods (unweighted minimum norm, WMN, and FOCUSS). Simulation results for single-source localization confirm the effectiveness and robustness of the proposed method with respect to the reconstruction accuracy of a simulated single dipole.

Multi-slices breast ultrasound lesion segmentation using Multi-Scale Vector Field Convolution snake

This study aims to apply a novel method called Multi-scale Vector Field Convolution Snake (MVFC) to segment breast ultrasound images using all slices presenting the lesion. The key idea is to combine the Vector Field Convolution Snake (VFC) method with a two-dimensional Gaussian filter with variable standard deviations in order to make snake models less sensitive to speckle noise and to contrast quality. Experimental results show that the form of the lesion changes from one slice to another which allows achieving greater precision in the extraction of the lesion characteristics.

Towards factor analysis exploration applied to positron emission tomography functional imaging for breast cancer characterization

The paper aims to explore the factor analysis when applied to a dynamic sequence of medical images obtained using nuclear imaging modality, Positron Emission Tomography (PET). This latter modality allows obtaining information on physiological phenomena, through the examination of radiotracer evolution during time. Factor analysis of dynamic medical images sequence (FADMIS) estimates the unerlying fundamental spatial distributions by factor images and the associated so-called fundamental functions (describing the signal variations) by factors. This method is based on an orthogonal analysis followed by an oblique analysis. The results of the FADMIS are physiological curves showing the evolution during time of radiotracer within homogeneous tissues distributions. This functional analysis of dynamic nuclear medical images is considered to be very efficient for cancer characterization, vascularization as well as possible evaluation of response to therapy.

Fault detection of chemical processes using KPCA-based GLRT technique

In this paper, we address the problem of nonlinear fault detection of chemical processes. The objective is to extend our previous work [1] to provide a better performance in terms of fault detection accuracies by developing a pre-image kernel PCA (KPCA)-based Generalized Likelihood Ratio Test (GLRT) technique. The benefit of the pre-image kPCA technique lies in its ability to compute the residual in the original space using the KPCA from the feature space. In addition, GLRT provides more accurate results in terms of fault detection. The performance of the developed pre-image KPCA-based GLRT fault detection technique is evaluated using simulated continuously stirred tank reactor (CSTR) model.

Bispectral analysis-based approach for steady-state visual evoked potentials detection

Brain-Computer Interface (BCI) systems are widely based on steady-state visual evoked potentials (SSVEP) detection using electroencephalography (EEG) signals. SSVEP-based BCIs are becoming attractive due to their higher signal-to-noise ratio (SNR) as well as faster information transfer rate (ITR). However, their performances are largely affected by the interference coming from the spontaneous EEG activities which intrinsically restrict their efficiency in distinguishing between SSVEPs and background EEG activities. In this paper, we introduce a new approach for the detection of SSVEP based on bispectral analysis to palliate the frequency-dependent bias. A COMB filter associated with a wavelet denoising filter is firstly used to minimize the noise while improving the SNR of phase signals. Next, the complementary orthogonal projections and the principle component analysis (PCA) are used to decompose the components related to SSVEPs and components related to brain activities. Finally, the bispectrum, a powerful tool for the analysis and the characterization of nonlinear properties of stochastic signals, is used to extract the features of the EEG signal benefiting from the information about the phase coupling of the signal components. The results of experiments, using two databases on five (or ten) subjects, show that the proposed approach significantly outperformed the standard CCA approach in distinguishing the target frequency and in average information transfer rate.

Damage detection in structural health monitoring using kernel PLS based GLR

The objective of this paper is to extend the applicability of the GLR method to a wide range of practical systems. Most real systems are nonlinear, multivariate, and are best represented by input-output type of models. Kernel partial least squares (KPLS) models have been widely used to represent such systems. Therefore, in this paper, kernel PLS-based GLR method will be utilized in practice to improve damage detection in Structural Health Monitoring (SHM). The developed kernel PLS-based GLR technique combines the benefits of the multivariate input-output kernel PLS model and the statistical fault detection GLR statistic which showed performance in the cases where process models are not available. GLR is a well-known statistical detection method that relies on maximizing the detection probability for a given false alarm rate. To calculate the kernel PLS model, we use the data collected from the complex 3DOF spring-mass-dashpot system. The simulation results show improved performance of kernel PLS-based GLR in damage detection compared to the classical kernel PLS method.

Fault detection in a wastewater treatment plant

In this paper, Unscented Kalman filter (UKF) based Exponentially Weighted Moving Average (EWMA) is proposed for fault detection in a Wastewater Treatment Plant (WWTP). In the developed UKF-based EWMA, the UKF technique is used to compute the residual between the true and the estimated variable and the EWMA control chart is applied to detect the faults. The fault detection technique will be tested using simulated COST wastewater treatment ASM1 model. The detection results of the UKF-based EWMA technique are evaluated using three fault detection criteria: the false alarm rate (FAR), Average Run Length (ARL1) and the missed detection rate (MDR).

High-resolution imaging-guided electroencephalography source localization: temporal effect regularization incorporation in LORETA inverse solution

Abstract. Electroencephalography (EEG) and magnetic resonance imaging (MRI) are noninvasive neuroimaging modalities. They are widely used and could be complementary. The fusion of these modalities may enhance some emerging research fields targeting the exploration better brain activities. Such research attracted various scientific investigators especially to provide a convivial and helpful advanced clinical-aid tool enabling better neurological explorations. Our present research was, in fact, in the context of EEG inverse problem resolution and investigated an advanced estimation methodology for the localization of the cerebral activity. Our focus was, therefore, on the integration of temporal priors to low-resolution brain electromagnetic tomography (LORETA) formalism and to solve the inverse problem in the EEG. The main idea behind our proposed method was in the integration of a temporal projection matrix within the LORETA weighting matrix. A hyperparameter is the principal fact for such a temporal integration, and its importance would be obvious when obtaining a regularized smoothness solution. Our experimental results clearly confirmed the impact of such an optimization procedure adopted for the temporal regularization parameter comparatively to the LORETA method.

A computer aided diagnosis ‘CAD’ for brain glioma exploration

Manual analysis of brain glioma tumor lacks accuracy and is time consuming. Thus, to avoid human error, this paper presents an automatic and accurate computer aided diagnosis (CAD) system for brain glioma exploration on magnetic resonance imaging. A preprocessing approach was proposed to eliminate extra-cerebral features. Tumor and even its edema was automatically extracted by a fast distribution matching algorithm. Then, we realize a 3D reconstruction for brain tumor glioma and its edema in order to classify glioma tumor based on their radiologic appearance. As discussed with clinicians, the experimental results showed that the proposed computer aided diagnosis for brain glioma tumor achieves a good agreement. Thus, the proposed tool facilitates and speeds up the analysis of data and supports decision making.