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The glaucoma is an eye disease that causes blindness when it progresses in an advanced stage. Early glaucoma diagnosis is essential to prevent the vision loss. However, early detection is not covered due to the lack of ophthalmologists and the limited accessibility to retinal image capture devices. In this paper, we present an automated method for glaucoma screening dedicated for Smartphone Captured Fundus Images (SCFIs). The implementation of the method into a smartphone associated to an optical lens for retina capturing leads to a mobile aided screening system for glaucoma. The challenge consists in insuring higher performance detection despite the moderate quality of SCFIs, with a reduced execution time to be adequate for the clinical use. The main idea consists in deducing glaucoma based on the vessel displacement inside the Optic Disk (OD), where the vessel tree remains sufficiently modeled on SCFIs. Within this objective, our major contribution consists in proposing: (1) a robust processing for locating vessel centroids in order to adequately model the vessel distribution, and (2) a feature vector that relevantly reflect two main glaucoma biomarkers in terms of vessel displacement. Furthermore, all processing steps are carefully chosen based on lower complexity, to be suitable for fast clinical screening. A first evaluation of our method is performed using the two public DRISHTI-DB and DRIONS-DB databases, where 99% and 95% accuracy, 96.77% and 97,5% specificity and 100% and 95% sensitivity are respectively achieved. Thereafter, the method is evaluated using two fundus image databases respectively captured through a smartphone and retinograph for the same persons. We achieve 100% accuracy using both databases which assesses the robustness of our method. In addition, the detection is performed on 0.027 and 0.029 second when executed respectively on the Samsung-M51 on the Samsung-A70 smartphone devices. Our proposed smartphone app provides a cost-effective and widely accessible mobile platform for early screening of glaucoma in remote clinics or areas with limited access to fundus cameras and ophthalmologists.

The right ventricular assessment is crucial to heart disease diagnosis. Unfortunately, its segmentation is quite challenging due to its intricate shape, ill‐defined thin edges, large variability among patients, and pathologies. Besides, it is a very laborious and time‐consuming task to be done manually. Therefore, automated segmentation techniques are very suitable to reduce the strain on the expert. Here, it is attempted to review the taxonomy of the current RV segmentation approaches adopted to handle the afore‐mentioned issues. Enhanced by our expert's interpretation, the results of over forty research papers were evaluated based on several metrics such as the dice metric and the Hausdorff distance. Synthetic tables and charts were also used to discuss the reviewed approaches. The following study shows that none of the existing methods has proved accurate enough to meet all the RV challenging issues. Many misestimated results were reported for several cases. Eventually, global guidance is outlined, which supports combining different methods to enhance the expected results during the MRI short‐axis slice processing.

The present work develops a novel hybrid method for ocular and muscular artifact removal from electroencephalography (EEG) signals, EFICA-TQWT. It is a combination of efficient fast independent component analysis (EFICA) method with the tunable Q-factor wavelet transform (TQWT). The main contribution of this paper is to apply the 3D interpolation method in the filtering system. Three EEG datasets are used in this work, two healthy and one epileptic. The choice of subjects for each dataset is made with the help of an expert in physiology. The selection criterion adopted is the presence of muscular and ocular artifacts in the processed recordings. First, a noisy channel automatic classification is performed by the support vector machine (SVM) with radial basis function in order to delete the signal(s) corresponding to the noisiest channel(s) from each EEG recording. The results of the automatic classification by the SVM were compared with those found by the expert’s classification. An accuracy of 97.45%, a sensitivity of 86.66% and a 100% specificity are provided by the SVM classification. The hybrid method of artifact removal will be applied on the rest of the EEG channels of international 10/20 system for each subject. Then, a reconstruction of the eliminated channel signal(s) will be performed in order to obtain a well-filtered signal. The proposed filtering process is evaluated by calculating the mean squared error (MSE) and the signal to noise ratio (SNR). Both for the healthy and pathological EEG datasets, a comparative study of the proposed method (EFICA-TQWT) and other filtering techniques (Fast-ICA, DWT, TQWT and EFICA) is generated. The EFICA-TQWT method gave the best results with a minimum of MSE and a maximum of SNR, more particularly in the case of the application of the 3D interpolation method. Besides, in order to optimize the computing time of the proposed system, a parallel implementation of this filtering system is developed based on graphical processing units using compute unified device architecture.

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.

International audience; Epilepsy is one of the most widespread neurological pathologies, which is characterized by localized or generalized brain dysfunction due to abnormal or excessive paroxysmal electrical discharges. These discharges lead to neuronal hyperactivities causing prolonged and involuntary muscle contractions or periods of loss or altered consciousness. A patient's quality of life could be greatly improved with an effective alarm system able to predict future crises. Our article presents a fully specified model for automated seizure prediction based on autoregressive multivariate modeling and stability state analysis from clinical and simulated electroencephalography (EEG) data. Our model allows the calculation of a stability index whose analysis in content makes it possible to predict crises. The approach is validated on 29 crises recorded for 8 patients from a database available in free access and 14 simulated subjects also called virtual patients generated by neuro-computer platform TVB (The Virtual Brain:www.thevirtualbrain.org) using Epileptor as mass neural model for gray matter and “john Doe” matrix as white matter. Our preliminary results exhibit a detection accuracy of 97.87 %, a sensitivity of 100 % and an average prediction time for the eight real patients of 237.21 s and a detection accuracy of 100 %, a sensitivity of 93 % and an average prediction time of 4.427 s for the simulated subjects.

Deep learning, the fastest growing segment of Artificial Neural Network (ANN), has led to the emergence of many machine learning applications and their implementation across multiple platforms such as CPUs, GPUs and reconfigurable hardware (Field-Programmable Gate Arrays or FPGAs). However, inspired by the structure and function of ANNs, large-scale deep learning topologies require a considerable amount of parallel processing, memory resources, high throughput and significant processing power. Consequently, in the context of real time hardware systems, it is crucial to find the right trade-off between performance, energy efficiency, fast development, and cost. Although limited in size and resources, several approaches have showed that FPGAs provide a good starting point for the development of future deep learning implementation architectures. Through this paper, we briefly review recent work related to the implementation of deep learning algorithms in FPGAs. We will analyze and compare the design requirements and features of existing topologies to finally propose development strategies and implementation architectures for better use of FPGA-based deep learning topologies. In this context, we will examine the frameworks used in these studies, which will allow testing a lot of topologies to finally arrive at the best implementation alternatives in terms of performance and energy efficiency.

In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neural network. The proposed approach, called systolic-SOM (SSOM), is based on the use of a generic model inspired by a systolic movement. This model is formed by two levels of nested parallelism of neurons and connections. Thus, this solution provides a distributed set of independent computations between the processing units called neuroprocessors (NPs) which define the SSOM architecture. The NP modules have an innovative architecture compared to those proposed in the literature. Indeed, each NP performs three different tasks without requiring additional external modules. To validate our approach, we evaluate the performance of several SOM network architectures after their integration on an FPGA support. This architecture has achieved a performance almost twice as fast as that obtained in the recent literature.

COVID-19 originally started in Wuhan city in China. The disease rapidly became a worldwide pandemic, causing a respiratory illness with symptoms such as coughing, fever, and in more severe cases difficulty in breathing. With the current testing processes, it is very difficult and sometimes impossible to manage and provide the necessary treatment to suspected patients since the number of the infected is rapidly increasing. Hence, the availability of an artificial intelligent driven system can be an assistive tool to provide accurate diagnosis using radiology imaging techniques. In this paper, we put forward a new deep learning architecture, which integrates the Nested Residual Connections (NRCs) in a DarkCovidNet model, called DarkCovidNet-NRC, in order to classify chest images and to detect COVID-19 cases. The proposed architecture is validated with the K-fold cross-validation technique on X-ray and CT chest datasets separately and then combined. The experimental results reveal that the suggested model performs very well in the medical classification task and it competes with the state of the art in multiple performance metrics by respectively achieving an accuracy and precision of 0.9609 and 0.978 on the combined dataset.

International audience; The segmentation of the retinal vascular tree presents a major step for detecting ocular pathologies. The clinical context expects higher segmentation performance with a reduced processing time. For higher accurate segmentation, several automated methods have been based on Deep Learning (DL) networks. However, the used convolutional layers bring to a higher computational complexity and so for execution times. For such need, this work presents a new DL based method for retinal vessel tree segmentation. Our main contribution consists in suggesting a new U-form DL architecture using lightweight convolution blocks in order to preserve a higher segmentation performance while reducing the computational complexity. As a second main contribution, preprocessing and data augmentation steps are proposed with respect to the retinal image and blood vessel characteristics. The proposed method is tested on DRIVE and STARE databases, which can achieve a better trade-off between the retinal blood vessel detection rate and the detection time with average accuracy of 0.978 and 0.98 in 0.59 s and 0.48 s per fundus image on GPU NVIDIA GTX 980 platforms, respectively for DRIVE and STARE database fundus images.

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.

In the era of the Internet of Things (IoT) and Big Data (BD), a significant amount of data is permanently generated every day. The data size of collected data streams is now reaching zetta bytes (i.e., 1021), and their processing and analysis becomes more and more challenging especially in embedded systems, where the overall goal is to maximize performance per watt, while meeting real-time requirements and trying to keep the overall power consumption in the very limited power budgets. The collected data are often reduced by means of clustering, vector quantization or compression before their further processing. The unsupervised learning techniques such as Self-Organizing Maps (SOMs) not needing any prior knowledge of processed data are perfect candidates for this task. However, real-time vector quantization with SOMs requires high performances and dynamic online configurability. The software counterparts of SOMs are highly flexible with limited performances per watt whereas the hardware SOMs generally lack of flexibility. In this paper, a novel scalable, dynamic and growing hardware self-organizing map (SOM) is presented. The presented hardware SOM architecture is dynamically configurable and adaptable in terms of neurons, map size and vector dimension depending on the application-specific needs. The proposed architecture is validated on different map sizes (up to 16×16) with different vector widths applied for real-time color quantization and pattern distribution recognition.

The LoRaWAN technology is today the object of great interest in the Internet of Things context. It defines a simple network architecture offering a wide-area wireless coverage for low rate IoT applications with low power consumption for devices. The LoRaWan class A is designed for sensor networks with a focus on the uplink. LoRaWan defines an optional MAC operation, Class B, that provides the network server with the opportunities to initiate a downlink, which can be a real solution for actuators focus network. Today, Performances of Class B are not quantified and compared to default LoRaWAN class. In this paper, we propose an evaluation of Class B performance. We offer a set of realistic evaluation scenarios based on an NS-3 simulation module that we have developed for this purpose. Results show that Class B reduces the delivery delay of downlink traffic in comparison to Class A. Class B operation significantly reduces the percentage of packet loss for downlink traffic even in congested contexts. We conclude that a trade off should be made between having low access delay or packet loss. Both the NS-3 module and data are released as an open-source to the research community.

International audience; The right ventricle (RV) is the largest heart cavity playing a vital role in the cardiac cycle and function. However, to assess its function in cardiac magnetic resonance imaging (CMRI), the segmentation is an essential but very challenging task due to the RV complex crescent shape, thin boundaries, as well as the high variability of images. Indeed, to overcome these issues, several approaches have been proposed. In this paper, we aim to highlight the impact of various short-axis slices on the segmentation process. For that sake, based on the Cvi42 advanced deep learning base software, we assess the effectiveness of the RV contour delineation process over an entire MRI cardiac short-axis slice. We consider for evaluating the accuracy, the Dice Score Metric (DSM), as well as some RV functional parameters such as the Ejection Fraction, the End-Systolic-Volume, and the End-Diastolic-Volume. Our experimental assessment is done using two CMRI exams of both healthy and diseased patients. We consider for evaluation the whole short-axis frames from base to apex and from end-systole to end-diastole. The evaluation results show that: (1) The segmentation accuracy is influenced by the slice level especially on the basal and apical slices, (2) A huge accuracy decreasing for the case of a patient having dysplasia.

Several leading-edge applications such as pathology detection, biometric identification, and face recognition are based mainly on blob and line detection. To address this problem, Eigen value computing has been commonly employed due to its accuracy and robustness. However, Eigen value computing requires a raised computational processing, intensive memory data access, and data overlapping, which involve higher execution times. To overcome these limitations, we propose in this paper a new parallel strategy to implement Eigen value computing using a graphics processing unit (GPU). Our contributions are (1) to optimize instruction scheduling to reduce the computation time, (2) to efficiently partition processing into blocks to increase the occupancy of streaming multiprocessors, (3) to provide efficient input data splitting on shared memory to benefit from its lower access time, and (4) to propose new data management of shared memory to avoid access memory conflict and reduce memory bank accesses. Experimental results show that our proposed GPU parallel strategy for Eigen value computing achieves speedups of 27 compared with a multithreaded implementation, of 16 compared with a predefined function in the OpenCV library, and of eight compared with a predefined function in the Cublas library, all of which are performed into a quad core multi-central-processing unit platform. Next, our parallel strategy is evaluated through an Eigen value-based method for retinal thick vessel segmentation, which is essential for detecting ocular pathologies. Eigen value computing is executed in 0.017 s when using Structured Analysis of the Retina database images. Accordingly, we achieved real-time thick retinal vessel segmentation with an average execution time of about 0.039 s.

International audience; The current challenge for image treatment, targeting the heart ventricles (LV and RV), consists on ensuring an accurate automatic segmentation aiming to identify the myocardial contours. In this paper we propose an automatic watershed-based segmentation of the left ventricle (LV)based on topology, geometry and brightness priors. We attempted to demonstrate the accuracy of our proposed approach by comparing it with other manual segmentations carried out by two clinical experts, on 20 patients. In the aim of obtaining an accurate 3D reconstruction, we propose the volumetric object reconstruction method using The Visualization Toolkit VTK .Furthermore, the 3D+t assessment is used for the examination of the dynamic behavior of the LV to reveal regions of myocardial dysfunction.

International audience; Watershed Transform is a widely used image segmentation technique that is known to be very data-intensive and time-consuming. The M-border Kernel Algorithm computes watersheds in the framework of Edge Weighted Graphs and allows to preserve the topology of the initial map. Par-allelization represents an effective solution to accelerate it. However, this task remains challenging due to the nature of this technique. In this paper, we address this problem. We start by analyzing the Data Dependency Issues that this algorithm raises when dealing with parallel execution. With respect to that, we propose a parallelization strategy that opts for vertices scanning instead of edges scanning of the graph while preserving the thinning paradigm on which the M-border Kernel Algorithm is based. We show that this strategy overcomes the problem of the simultaneous lowering of two adjacent M-border edges that may occur when edges scan is used. The implementation of the proposed algorithm on a shared memory multicore architecture proves its effectiveness in terms of speedup. In fact, the experimental results show that a 2 Yosra Braham et al. speedup factor of 5.55 is achieved using 8 processors for 2048 × 2048 images over the performance of the sequential algorithm using a single processor on the same architecture. Furthermore, the gain in terms of execution time and thus speedup is guaranteed whatever is the size of images on which the algorithm is applied. In fact, a speedup factor of 5.55 is obtained for 2048 × 2048 images, 5.11 for 1024 × 1024 images and 4.45 for 512 × 512 images using 8 cores.

Due to their ability to reduce the size of high-dimensional input data, self-organizing maps (SOMs) can be employed as data quantizers. The widely used software implementations of SOM enjoy flexibility and adaptability, usually to the detriment of performances, which limits their use in real time applications. On the contrary, the hardware counterparts of SOMs exploit the inherent parallelism of hardware to boost the overall performances, but generally lack adaptability without considerable design efforts. To benefit from both, the flexibility of software and performances of hardware SOM implementations, unconventional design approaches of SOMs should be used. In this work, a scalable and adaptable hardware implementation of a SOM network is presented. The proposed architecture allows to dynamically extend the SOM operation from a smaller to a larger map only by (re-)configuring the parameters of each neuron. The gained scalability is obtained by decoupling the computation layer composed of neurons, from the communication one, used to provide data exchange mechanisms between neurons. The proposed SOM architecture is also validated through simulation on variable-sized SOM networks applied to image compression.

This study presents an architecture-optimising methodology for embedding an learning vector quantization (LVQ) neural network on an field programmable gate array (FPGA) device. The embedded architecture contains both learning and decision circuitry and is optimised towards the lowest power/energy consumption. The low-power/energy architecture is obtained through the selection of the best one amongst a number of architectures produced by FPGA software design tools that combine power, area and the ergonomic utilisation of internal FPGA resources. A complete characterisation of power at the architectural level was carried out using the Xpower tool. An analytical power model was determined by the following parameters: area, delay and LVQ topology. Concerning the authors’ architecture, there is a 28% gain in the area design. Moreover, it consumes 8% power in the nanoboard 3000 compared with the other ones.

Context Cardiovascular disease remains the leading cause of death affecting the adult population. It involves disorders of myocardial perfusion which can lead to the disturbance in myocardial function and/or myocardial infarction. Finding an efficient method for the deformation analysis in the left ventricle of the heart (LV) is one of the major concerns in cardiac imaging. Objective In literature, most models provide global representation of the LV surface, that cannot specify the affected area. The present work is aimed at quantifying the local deformations in the LV by developing accurate computational approaches in order to refine the diagnosis and specify the ischemic territory. Method The proposed approach advocates the application of three different methods: (1) the numerical quantification of regional deformation based on SPHARM shape descriptors, (2) volume evolution, and (3) the Hotelling metric computation by use of the relative curvature at the vertices of the triangulation. In order to progress to a regional analysis, we carried out a division into 17 regions according to the AHA standard of LV object. It is worth noting that our two first methods are based on the application of the cloud closing process by generating new points on the cutting planes. Results The different approaches were tested on 44 patients. The obtained results show its effectiveness and validity by giving the degree and the location of the pathological deformation in the LV. We compared the obtained results with diagnostic offered by qualified nuclear medicine physicians. Also, we compared the obtained results of each methods in order to define the more robust approach.

Grammar and the theory of language have been around for a long time. So far, grammar formalism has been used for programming and formal languages to describe natural languages and software engineering. However, the grammatical discipline can be extended to join other areas of applications such as ECG signal analysis. Thus, this chapter will provide an overview of the related methods and show that syntactic methods and regular grammar can be supported for ECG analysis and especially for the recognition of QRS complexes. A comparative study with a huge number of known methods will be presented.

In this work, a scalable and adaptable hardware SOM architecture allowing to execute multiple applications in parallel is presented. The proposed architecture allows to use simultaneously multiple SOM structures with different parameters in order to satisfy multiple applications with different needs. The application switching is done within a clock cycle at the neuron’s level at run time only by analyzing the received input data. The proposed architecture was tested and validated in an image quantization experiment where 6 quantization applications with different parameters (from 6 × 6 to 15 × 15 SOMs with inputs varying from 3 to 12 elements) were performed simultaneously.

In this paper, electrocardiogram (ECG) analyses were used as valuable a tool in the evaluation of cognitive tasks also given by the electroencephalograms (EEG). By taking and analyzing measurements in large quantities, we tried to better understand the functioning of human physiological systems. This study examined the cognitive and cardiovascular system function simultaneously. The purpose of this paper was to seek statistical causality in the sense of Granger between the EEG and ECG signals based on time series and autoregressive vector processes (VAR). For this purpose, 24 hours were recorded and during the tests, random and non-stationary portions of the ECG and EEG were extracted. The results indicated that there was Granger causality between the signals. This allowed us to forecast and predict traffic spots within and between the ECG and EEG signals.

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.

X-ray Coronary angiograms are intended to specify the global state of the artery system and therefore to detect and locate the zones of narrowing. Accurate coronary artery segmentation is a fundamental step in computer aided diagnosis of many diseases. In this paper, deep neural network based on U-Net architecture is proposed in order to improve the segmentation task for coronary images. In this context, various enhancement methods, like the adaptive histogram equalization and the multiscale technique with a Frangi filter are tested not only in normal conditions but also in the presence of noise to improve the system performance and to ensure its robustness against real conditions. Promising result are obtained and discussed for different performance criteria. This work will serve as a reference and motivation for researchers interested in the field of blood vessel segmentation by deep learning neural networks.

In this paper we present a new architectural approach of a Self-Organizing Map (SOM). The proposed architecture, called Systolic-SOM (SSOM), is based on a generic formalism that exploits two levels of nested parallelism of neurons and connections. Thus, this solution provides a distributed set of independent computations between the neuroprocessors that define the SSOM architecture. To validate our approach, we evaluate the performance of several SOM network architectures after their integration on FPGA support. This architecture has achieved a performance almost twice as fast as that obtained in recent literature.

The development of hardware platforms for artificial neural networks (ANN) has been hindered by the high consumption of power and hardware resources. In this paper, we present a methodology for ANN-optimized implementation, of a learning vector quantization (LVQ) type on a field-programmable gate array (FPGA) device. The aim was to provide an intelligent embedded system for real-time vigilance state classification of a subject from an analysis of the electroencephalogram signal. The present approach consists in applying the extension of the algorithm architecture adequacy (AAA) methodology with the arithmetic accuracy constraint, allowing the LVQ-optimized implementation on the FPGA. This extension improves the optimization phase of the AAA methodology by taking into account the operations wordlength required by applying and creating approximative-wordlength operation groups, where the operations in the same group will be performed with the same operator. This LVQ implementation will allow a considerable gain of circuit resources, power and maximum frequency while respecting the time and accuracy constraints. To validate our approach, the LVQ implementation has been tried for several network topologies on two Virtex devices. The accuracy–success rate relation has been studied and reported.

In this work, a new method for removal of ocular and muscular artifacts from Multi-channel electroencephalogram (EEG) is presented in order to obtain a 3D filtered cerebral mapping images. First, a FastICA algorithm of Independent Component Analysis (ICA) is applied in combination with the tunable Q-factor wavelet transform (TQWT), FastICA-TQWT. Then, to show the robustness of this method, a comparison was made between the proposed FastICA-TQWT method and the classical one FastICA-DWT based on three criteria: Mean Squared Error (MSE), correlation coefficient and Signal Noise Ratio (SNR). The results showed that the FastICA-TQWT method gave the highest Signal Noise Ratio and correlation coefficient and the minimum Mean Squared Error. However, the FastICA-TQWT algorithm requires an extremely high computing power. Therefore, the second contribution of this paper is to provide an EEG signal treatment by implementing the hybrid FastICA-TQWT algorithm using a new computing technology designed for a high-performance computing, called Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) technology. The performance of the parallel approach running along the GPU was compared to a CPU implementation.

International audience; The watershed transform is considered as the most appropriate method for image seg-mentation in the field of mathematical morphology. In the following paper, we present an adapted topological watershed algorithm suited for a rapid and effective implementation on Shared Memory Parallel Machine (SMPM). The introduced algorithm allows a parallel watershed computing while preserving the given topology. No prior minima extraction is needed, nor the use of any sorting step or hierarchical queue. The strategy that guides the parallel watershed computing, labeled SDM-Strategy (equivalent to Split-Distributes and Merge), is also presented. Experimental analyses such as execution time, performance enhancement , cache consumption, efficiency and scalability are also presented and discussed.

International audience; This special issue on Real-Time Processing of Medical Images addresses the current state-of-the-art in the field of parallel programming for medical imaging applications and the future trends in real-time medical image processing, including parallel computing (computational models, parallel algorithms and hierarchical memory optimizations), real-time implementation of embedded medical image processing applications on dedicated architectures and/or parallel architectures. We are very grateful to the reviewers who provided valuable comments and suggestions to improve the quality of the papers accepted for this special issue. Brief outlines of these papers are stated below: The first paper by Yee Hui Lee, Mohamed Khalil-Hani, Rabia Bakhteri and Vishnu P. Nambiar presents a real-time image acquisition system with an improved image quality assessment module to acquire high quality Near Infrared Images. The NIR image quality assessment module utilizes improved two-dimensional entropy and mask-based edge detection algorithms. In this work, the effectiveness of the proposed NIR image acquisition system is demonstrated through the implementation of a complete finger-vein biometric authentication system. The proposed module is implemented as an embedded system on a Field Programmable Gate Array (FPGA). The proposed system is able to achieve a recognition accuracy of 0.87% equal error rate (EER) and can handle real-time processing at 15 frames/second. The second paper by Faten Chaieb, Tarek Ben Said, Sabra Mabrouk and Faouzi Ghorbel presents a segmentation method from four-phase computed tomography images. The proposed method is based on the combination of the Expectation – Maximization algorithm and the Hidden Markov Random Fields. The segmentation method is applied on a Volume Of Interest (VOI) that decreases the number of processed voxels. In order to accelerate the classification steps within the segmentation process, a bootstrap resampling scheme is applied. The proposed method is speeded up by adopting a bootstrap sampling approach on a semi-automatic selected VOI.

Background The sequence of Q, R, and S peaks (QRS) complex detection is a crucial procedure in electrocardiogram (ECG) processing and analysis. We propose a novel approach for QRS complex detection based on the deterministic finite automata with the addition of some constraints. This paper confirms that regular grammar is useful for extracting QRS complexes and interpreting normalized ECG signals. A QRS is assimilated to a pair of adjacent peaks which meet certain criteria of standard deviation and duration. Results The proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks’ amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed. Conclusions Regular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited in the literature.

In this paper we propose a new multi-scale fully automatic algorithm based on Graph cuts for vessel extraction. In fact, we combine vesselness, geodesic paths, a multi-scale edgeness map and the directional information for vessel tracking in order to personalize the Graph cuts approach to the segmentation of tubular structures.

The Multiple-Wordlength Operation Grouping (MWOG) is a recently used approach for an optimized implementation on a Field Programmable Gate Array (FPGA). By fixing the precision constraint, this approach allows minimizing the data wordlength. In this paper, the authors present the integration of the approach based on the MWOG in the Algorithm Architecture Adequation (AAA) methodology, designed to implement real-time applications onto reconfigurable circuits. This new AAA-MWOG methodology will improve the optimization phase of the AAA methodology by taking into account the data wordlength and creating approximative-wordlength operation groups, where the operations in the same group will be performed with the same operator. The AAA-MWOG methodology will allow a considerable gain of circuit resources. This contribution is demonstrated by implementing the Learning Vector Quantization (LVQ) neural-networks model on the FPGA. The LVQ optimization is used to quantify vigilance states starting from processing the electroencephalographic signal. The precision-gain relation has been studied and reported.

In this paper, we present a new generic architectural approach of a Self-Organizing Map (SOM). The proposed architecture, called the Diagonal-SOM (D-SOM), is described as an Hardware–Description-Language as an intellectual property kernel with easily adjustable parameters.The D-SOM architecture is based on a generic formalism that exploits two levels of the nested parallelism of neurons and connections. This solution is therefore considered as a system based on the cooperation of a distributed set of independent computations. The organization and structure of these calculations process an oriented data flow in order to find a better treatment distribution between different neuroprocessors. To validate the D-SOM architecture, we evaluate the performance of several SOM network architectures after their integration on a Xilinx Virtex-7 Field Programmable Gate Array support. The proposed solution allows the easy adaptation of learning to a large number of SOM topologies without any considerable design effort. [Formula: see text] SOM hardware is validated through FPGA implementation, where temporal performance is almost twice as fast as that obtained in the recent literature. The suggested D-SOM architecture is also validated through simulation on variable-sized SOM networks applied to color vector quantization.

This paper describes the architecture design of novel massively parallel self-organizing map (SOM) neural networks. The proposed architecture, referred to as the planar SOM (PSOM), is described as a soft IP core synthesized in VHDL. The SOM neural network’s size and the input data vectors’ dimension are adjustable parameters. In this work, several SOM architectures are synthesized and their performance is evaluated for Xilinx Virtex-7 FPGAs. The presented hardware architecture allows online learning and can be easily adapted to a large variety of SOM topologies without a considerable design effort. A [Formula: see text] SOM hardware is validated through the FPGA implementation and its performances with an estimated working frequency of 297[Formula: see text]MHz for a 23-element input vector will reach 21,970 MCUPS in the learning phase and 35,902 MCPS in the recall one.

We propose in this work a method of electrocardiogram (ECG) signal pretreatment by the application of Discreet Wavelet Transform DWT by automatically determining the optimal order of decomposition. After the purification of the original signal, we describe an algorithm to detect R waves based on the Dyadic Wavelet Transform DyWT by applying a windowing process. This algorithm is validated on a sample of synthesis ECG signal with and without noise which we have proposed and on real data. Finally, once the R peaks of real data are detected, we use three methods of RR intervals analysis by calculating the standard deviation of heart rate and applying the Fast Fourier Transform FFT and the Wavelet Transform on detected RR intervals to study the Heart Rate Variability (HRV). A comparative study between the analysis results of detected RR intervals in healthy and diseased subjects through the application of the FFT and the Wavelet Transform will be given.

Several methods for detecting the face and extracting the facial features and components exist in the literature. These methods are different in their complexity, performance , type and nature of the images and the targeted application. The facial features and componentsare used in security applications, robotics and assistance for the disabled. We use these components and characteristics to determine the state of alertness and fatigue for medical diagnoses. In this work we use plain color background images whose color is different from the skin and which contain a single face. We are interested inFPGA implementation of this application. This implementation must meet two const raints, which are the execution time and the FPGA resources. We have selected and have associated a face detection algorithm based on theskin detection (using the RGB space) with a facial-feature extraction algorithm based on trackingthe gradient and the geometric model.

Watershed transform is widely used in image segmentation. In literature, this transform is computed by various algorithms among which the M-border kernel algorithm [1]. This algorithm computes the watershed transform in the framework of edge weighted graphs. It is based on a local property that makes it adapted to parallelization. In this paper we propose a parallel implementation of this algorithm. We start by studying the data dependencies problematic that it raises. We give then an approach that allows overcoming this problematic based on an alternated edges processing strategy. The implementation of this strategy on a shared memory multicore architecture using a Single Program Multiple Data (SPMD) approach proves its effectiveness. In fact, experimental results show that our implementation achieves a relative speedup factor of 2.8 using 4 processors over the performance of the sequential algorithm using a single processor on the same system.

The choice of a segmentation method depends on several considerations, namely the nature of the image, the primitives to extract and the segmentation methods. We propose an MLP-basis neuronal approach for the choice of the segmentation method taking into account the nature of the input image. First, an evaluation of the quality of segmentation by different methods and using various criteria of evaluation was carried out. Then, a characterization of images, based on some objective parameters, was performed. The resulting descriptors will be used as input to the neuronal approach to associate each type of image with the adequate segmentation method after learning. We report the results of the intelligent segmentation method choice obtained on different databases of medical images. The discussion of these encouraging results allowed us to improve our success rate and cover all varieties of images.

Today a need is emerging for embedding electronic and sensor functions in the products which needs these functions, and, importantly, to do this without noticeably influencing the mechanical design of the product. This contribution describes an approach used to produce a 2.5/3D free-form rigid and smart objects or randomly shaped circuit. The proposed fabrication process of shaped circuit is compatible with a typical printed circuit manufacturing and electronics assembly. Once the circuit is completed in its flat shape, its random final functional shape is given using thermoforming. In order to be able to deform a given flat circuit to its final form with predictable final spatial positions of components and interconnections. A FEM simulation is conducted to model the thermoforming of polymer based electronic circuits. As one of the process outputs, the wall thickness distribution predicted for the final part is compared with the experimental results.

International audience; Accurate coronary artery segmentation is a fundamental step in various medical imaging applications such as stenosis detection, 3D reconstruction and cardiac dynamics assessing. In this paper, a MultiScale Region Growing (MSRG) method for coronary artery segmentation in 2D X-Ray angiograms is proposed. First, a region growing rule which incorporates both vesselness and direction information in a unique way is introduced. Then an iterative multiscale search based on this criterion is performed. Selected points in each step are considered as seeds for the following step. By combining vesselness and direction information in the growing rule, this method is able to avoid blockage caused by low vesselness values in vascular regions, which in turn, yields continuous vessel tree. Performing the process in a multiscale fashion helps extracting thin and peripheral vessels often missed by other segmentation methods. Quantitative evaluation performed on real angiography images shows that the proposed segmentation method identifies about 80% of the total coronary artery tree in relatively easy images and 70% in challenging cases with a mean precision of 82% and outperforms others segmentation methods in terms of sensitivity. The MSRG segmentation method was also implemented with different enhancement filters and it has been shown that the Frangi filter gives better results. The proposed segmentation method has proven to be tailored for coronary artery segmentation. It keeps an acceptable performance when dealing with challenging situations such as noise, stenosis and poor contrast.

International audience; In this paper, a parallel hardware implementation of a self-organizing map (SOM) is presented. Practical scalability and flexibility are the main architecture features which are obtained by using a Network-on-chip (NoC) approach for communication between neurons. The presented hardware architecture allows on-line learning and can be easily adapted for a large variety of applications without a considerable design effort. A hardware 5 × 5 SOM was validated through the FPGA implementation and its performances at a working frequency of 200MHz for a 32-element input vector reach 724 MCUPS in the learning and 1168 MCPS in the recall phase.

International audience; —Several high-performance applications integrate loop bodies which represent the most critical sections. This aspect brings two challenges. First, the Worst Case Execution Time (WCET) must be determined in order to define the nested loop timing behaviour. The second challenge consists in raising the parallelism-level to enhance the performance. In particular, the Multidimensional Retiming (MR) is an important optimization approach that offers several instruction-level-parallelism solutions. Despite the fact that full parallelism allows achieving the optimal WCET, it leads to a high growth in processing cores, which is inadequate to embedded real-time implementations. The main idea of this paper consists in driving the parallelism-level rise in terms of WCET development. First, the MR parameters that correspond to the nested loops are extracted. Thereafter, the WCET is formulated in term of the parallelism level rise. Then, an optimization heuristic is proposed which identifies the parallelism level that allows respecting the WCET constraint. Our experiments indicate the WCET prediction is accurate within an error rate of 8.54%. Secondly, the optimization heuristic implementations show an average improvement on number of cores of 27.18% compared to full parallel ones.

International audience; — Multidimensional retiming is an efficient optimization approach that ensures increasing a parallelism level in order to optimize the execution time. Two existing techniques called incremental and chained multidimensional retiming are based on this approach, which aim at achieving a full parallelism on loop body in order to schedule applications with a minimum cycle period. However, the cycle number increases in terms of parallelism level which presents a limiting factor to respect the execution time constraint of real-time applications. In this paper, we show how the minimal cycle period is achieved in multidimensional applications without applying a full parallelism. We present the theory of a novel technique, called delayed multidimensional retiming. Firstly, two efficient algorithms are presented where the first one insures the extraction of timing and data dependency properties of the application and the second one selects the set of data path for retiming. Then, we propose theorems to deduce a retiming function for the selected paths. Finally, a third algorithm describing the optimization approach is introduced. The experimental results show that our technique improves execution times in comparison to existing techniques. It achieves average improvements on the execution time of 41.57% compared to the Incremental technique and 11.55% compared to the Chained technique.

International audience; Image processing technology has grown significantly over the past decade. Its application on low-power mobile devices has been the interest of a wide research group related to newly emerging contexts such as augmented reality, visual search, object recognition, and so on. With the emergence of general-purpose computing on embedded GPUs and their programming models like OpenGL ES 2.0 and OpenCL, mobile processors are gaining a more parallel computing capability. Thereby, the adaptation of these advancements for accelerating mobile image processing algorithms has become actually an important topical issue. In this paper, our interest is based on reviewing recent challenging tasks related to mobile image processing using both serial and parallel computing approaches in several emerging application contexts.

This paper deals with an image segmentation tool inspired from grammar formalism and based on spiral matrix. We are to set a scintigraphic image as a set of lexemes based on a vocabulary of intensities and a set of grammatical rules. Thus, the endocardium, epicardium and epicardial muscle edges are detected. In addition, a set of quantitative information is also deduced such as endocardium area, endocardium diameter and epicardial muscle thickness. This type of task is intended for medical diagnosis assistance. To validate our method, we have segmented a set of real scintigraphic images.