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Electroencephalography (EEG) is one of the most signals used for studying and demonstrating the electrical activity of the brain due to the absence of side effects, its noninvasive nature and its well temporal resolution. Indeed, it provides real-time information, so it can be easily suitable for predicting drivers’ vigilance states. The classification of these states through this signal requires sophisticated approaches in order to achieve the best prediction performance. Furthermore, deep learning (DL) approaches have shown a good performance in learning the high-level features of the EEG signal and in resolving classification issues. In this paper, we will predict individuals’ states of vigilance based on the study of their brain activity by analyzing EEG signals using DL architectures. In fact, we propose two types of networks: (i) a 1D-UNet model, which is composed only of deep one-dimensional convolutional neural network (1D-CNN) layers and (ii) 1D-UNet-long short-term memory (1D-UNet-LSTM) that combines the proposed 1D-UNet architecture with the LSTM recurrent model. The experimental results reveal that the suggested models can stabilize the training model, well recognize the subject vigilance states and compete with the state of art on multiple performance metrics. The per-class average of precision and recall can be, respectively, up to 86% with 1D-UNet and 85% with 1D-UNet-LSTM, hence the effectiveness of the proposed methods. In order to complete our virtual prototyping and to get a real evaluation of our alert equipment, these proposed DL models are implemented also on a Raspberry Pi3 device allowing measuring the execution time necessary for predicting the state vigilance in real time.

Segmentation of cardiac magnetic resonance images (cMRI) remains a challenging task in the field of scientific research due to its significance in the medical assessment of cardiovascular diseases. Ensuring accurate segmentation of the heart structures, mainly the left ventricle cavity, serves to extract important information and has a major impact on the quantitative analysis of the heart function which helps to conduct the proper diagnosis of doctors. The present paper introduces a simple and efficient U-shaped convolutional neural network aiming to accurately segment the LV from cMR images. We applied our architecture for Left Ventricle (LV) segmentation on cardiac MR images (cMRI), from the Automated Cardiac Diagnosis Challenge (ACDC). Obtained results are promising. This simple model based on CNN has significantly fewer parameters rendering it less demanding in terms of computation. Nevertheless, it has provided accurate segmentation. The tested method achieved LV Dice scores of 0.958 at end-systolic time (ES) and 0.979 at end-diastolic time (ED), which yields a mean Dice score of 0.968 on the ACDC dataset.

This research is based on a hemodynamic study of liver donor patient’s normal venous blood flow. The aim is to build a 3D model of the venous system, in order to visualize the blood pressure’s variation as well as its velocity in the liver through a CFD (Computational Fluid Dynamics) study. In this case, we are going to propose an upcoming study explaining the phenomenon of portal hypertension of the receiver’s liver patient which led to his suffering from Esophageal varices.

In the present study, we investigated the velocity profile over the carotid bifurcation in ten healthy volunteers by combining velocity measurements from two imaging modalities (PC-MRI and US-Doppler) and hemodynamic modeling in order to determine the optimal combination for the most realistic velocity estimation. The workflow includes data acquisition, velocity profile extraction at three sites (CCA, ECA and ICA), the arterial geometrical model reconstruction, a mesh generation and a rheological modeling. The results showed that US-Doppler measurements yielded higher velocity values as compared to PC-MRI (about 26% shift in CCA, 52% in ECA and 53% in ICA). This implies higher simulated velocities based on US-Doppler inlet as compared to simulated velocities based on PC-MRI inlet. Overall, PC-MRI inlet based simulations are closer to measurements than US-Doppler inlet based simulations. Moreover, the measured velocities showed that blood flow keeps a parabolic sectional profile distal from CCA, ECA and ICA, while being quite disturbed in the carotid sinus with a significant decrease in magnitude making this site very prone to atherosclerosis.

The aim of this study is the microcalcification segmentation in digital mammograms. We propose two different methods which are based on the combination of the multifractal analysis with, respectively, the fractal analysis and then with the lacunarity. Our approach consists of two steps. On the first stage, we created the “α_image.” This image was constructed by singularity coefficient deduced from multifractal spectrum of the original image. On the second stage, in order to enhance the visualization of microcalcifications, we create the “f(α)_image” based on global regularity measure of “α_image.” Two different techniques are used: the box counting (BC) used to calculate fractal dimension and the gliding box method used to measure lacunarity. These techniques were applied in order to compare results. Our proposed approaches were tested on mammograms from “MiniMIAS database.” Results demonstrate that microcalcifications were successfully segmented.

One of the major concerns in cardiac imaging is finding an efficient method for the deformation analysis in the left ventricle of the heart (LV). This paper deals with the results of using a regional analysis of left ventricular of the heart applied on MRI data. Our analysis consists of segmenting 3D objects modeling the myocardium of the LV into 17 regions according to the AHA (American Heart Association) standard. Curvature variation was calculated using the Hotelling MT2 two samples difference metric for the global objects and their different 17 regions during 25 instants of the cardiac cycle. It has been validated with real MRI data. Experimental results demonstrate the great robustness and efficiency of our method to detect pathological regions of myocardium.

Heart rates have normal values ranging from 60 to 80 beats per minute (bpm) for adults. RR distances have normal durations between 0.75 and 1 second. The complexes QRS durations have normal durations of less than 0.1 second. However, heart rate and RR distances also depend on age (adult or child), the patient's status (rest or stress), sex (male or female) and the patient's conditions (healthy or pathological). Heart rates, RR distances and QRS durations are not sufficient to determine whether ECGs are normal or pathological. Recently, two novel metrics have been calculated to reflect the regularity of RR distances and the QRS complexes durations irrespective of the patient's age, sex and heart rate. In this paper, these novel parameters were tested and validated on the arrhythmia (MIT-BIH), Abdominal and Direct Fetal ECG (ADFECGDB) and BIDMC Congestive Heart Failure (CHFDB) databases.

This work applies a novel method called multiquadratic interpolation that represents a 3D brain activity following a spatiotemporal mode. It also develops other classical interpolation techniques (barycentric, spline), which are based on the calculation of the Euclidean distance between the estimated and measured electrodes. Then, it modifies these methods by substituting the Euclidean distance by the corresponding arc length. Starting from 19 real electrodes for generating the electroencephalogram (EEG) potential representations of healthy subjects having three different behavioral brain states, a 3D EEG mapping of 128 electrodes was obtained. The proposed multiquadratic interpolation is evaluated by comparing it with the other methods by calculating the root mean squared error and processing time means.

This paper deals with the hemodynamic modeling in a carotid artery bifurcation with two laws - the laminar Newtonian and non Newtonian blood flow models- and two velocity inlet conditions - uniform and parabolic -. We assumed that blood flow is laminar and incompressible and the wall is rigid. From the artery geometry and the inlet velocity profile, four hemodynamic models of pulsatile flow were performed. These models were tested on a healthy subject data. The vascular model was reconstructed from Computed Tomography Angiography acquisition. The Doppler Ultrasound velocity data provided were considered as references. As a first conclusion, in the internal carotid artery, simulated velocities with non Newtonian law and uniform velocity inlet were the closest to US-Doppler measurements. However, in the external carotid artery, the closest model to US-Doppler measurements was non Newtonian law with parabolic inlet. Overall, Newtonian and non Newtonian blood flow models had a close behavior.

This paper deals with patient-specific blood flow modeling in a stenosed internal carotid artery (ICA). An ICA stenosis has an impact on hemodynamic behavior. It can hamper the brain irrigation and even cause a stroke. Our aim is to predict the blood flow behavior through computational fluid dynamic (CFD) study. The proposed approach realizes a hemodynamic modeling within a geometric carotid model build from a 3D computed tomography angiography image with blood considered as a Newtonian and incompressible fluid, and the wall as rigid. The blood flow modeling is based on the Navier-Stokes equation. A Womersley velocity profile is used as a boundary condition in the common carotid artery. Main results of this study are the following: (1) velocity is maximum in stenosis and minimum in the sinus, (2) pressure is negative on the most of carotid artery bifurcation unless the post-stenotic site.

Osteoporosis is a disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The goal is to analyse according to clinical research a group of 174 subjects: 87 osteoporotic patients with various bone fracture types and 87 healthy subjects. In order to characterize osteoporosis, a method of lacunarity measurement for grayscale image is used. This approach allowed the discrimination between healthy subjects and patients with osteoporosis. The results show an improved classification rate compared to another work [1].

This paper presents a robust method of local deformation analysis of the heart's left ventricle (LV) aimed at specifying the affected area. Our method is based on regional curvature variation calculation using the Hotelling T2 two samples difference metric. It has been validated with real data obtained from myocardial scintigraphy imaging techniques of 44 patients. For each patient we extract 3D surfaces representing the anatomical structures of interest, namely the endocardium and epicardium at stress. In order to progress to a regional analysis, we carried out, for each patient, a division into 17 regions according to the AHA standard. Experimental results demonstrate the great robustness and efficiency of our method.

The aim of this study is the detection and segmentation of microcalcifications in digital mammograms using multifractal analysis. To detect the suspicious Region Of Interest (ROI), containing anomalies, we propose to decompose the whole image into ROIs and compare the multifractal spectrums based on the q-structure functions of each one. The segmentation of microcalcifications consists of two steps. On the first step, we create an image denoted ‘α_image’. This image is constructed using the singularity coefficient, deduced from multifractal spectrum. Then, in the next step, we enhance the visualization of microcalcifications by creating an image denoted ‘f(α)_image’ based on the global regularity measure of the ‘α_image’ spectrum. We investigated the robustness of our approach using a data set of mammograms from ‘MiniMIAS’ database. Results demonstrate the accuracy of our approach, which successfully detect and segment microcalcifications with irregular form and small size.

This paper presents a new approach for synthesizing breast tissue images based on a random fractal process, the fractional Brownian motion (fBm). This work deals with modeling Regions of Interest (ROIs) of mammographic images. Diverse synthetic ROIs were generated: healthy ones and others with microcalcifications according to fatty and dense tissue. Microcalcifications were injected in several dispositions in order to model benign and malignant cases. The aim of this study resides in two points: (1) the generation of synthetic images of mammograms for researchers and radiologists in order to test their tools and orient the choice of their parameters to enhance the diagnostic accuracy; and (2) to compare two microcalcification segmentation approaches: ‘Sq-Sq’ approach based on multifractal analysis and the ‘MM’ approach based on Mathematical Morphology. In fact, the results proved that the ‘Sq-Sq’ method can detect microcalcifications with different arrangements for any type of tissue and were evaluated using a qualitative test by an expert and a quantitative one based on the Area Overlap Measure (AOM) and the Dice coefficient. The ‘Sq-Sq’ approach yield a mean of 0.8±0.06 for AOM and 0.8446 for Dice coefficient for all segmented images.

A closed surface of a 3D object with spherical topology can be expressed into a series of spherical harmonic (SPHARM) functions. The SPHARM descriptors allow accurate representation of large data sets with small number of coefficients. In this paper, we present a new approach for regional deformation analysis of the heart left ventricle (LV). We use spherical harmonics (SPHARM) shape descriptors with the modified Hotelling T2 metric in order to characterize the myocardium disease extent and its severity. This approach has been validated with real data obtained from myocardial scintigraphy imaging techniques. The obtained results show its effectiveness and validity.

We propose in this work two main interpolation methods (barycentric, spline) applicable to 3D EEG mapping. These 3D methods are used to interpolate scalp potential activity in order to obtain a more efficient spatiotemporal resolution of EEG mapping. Starting from 19 electrodes to generate the potential representations of patients having various behavioral states, we obtain a 3D potential representation of 128 electrodes thanks to the 3D interpolation methods. The evaluation of these algorithms is realized by calculating the normalized Root Mean Squared error (RMS).