ANALYSE MULTI FRACTALE DES ÉCHOS RADAR PAR LA MÉTHODE DES MAXIMUMS DES MODULES DE LA TRANSFORMÉE EN ONDELETTE (MMTO) 2D POUR LES SITES DE BORDEAUX (FRANCE), SÉTIF (ALGÉRIE) : APPLICATION À L'ÉLIMINATION DES ÉCHOS PARASITES

International audienceIn this work, the 2D-WTMM multifractal approach was applied to analysis the radar echoes, and to identify the unwanted echoes coming from terrestrial surface. With this intention, we considered radar images taken from two areas where different climates and relief prevail. We showed that almost Anaprops are characterized by a monofractal spectrum contrary to the echoes of precipitations which present a multifractal character. Moreover, we showed that the Holder coefficient and the combination of the spectrum mode and density of skeleton per pixel present robust factors to discriminate between the two types of echoes. Indeed, the unwanted echoes are practically eliminated at 98 per cent whereas the echoes of precipitation are almost preserved at 98,2 per cent. Also, we showed that the error between the measured intensity on the ground and the estimated intensity after treatment of the unwanted echoes does not exceed 5% for the Sétif site. Because the computation time is three minutes, the radar images can be processed in real-time.Dans le présent travail, l'approche MMTO-2d est appliquée pour l'analyse multi fractale des échos radar et l'identification des échos parasites en provenance de la surface terrestre. Pour ce faire, nous avons considéré des images radar prises dans deux régions où prévalent des climats et des reliefs différents. Il s'agit des sites de Sétif (Algérie) et Bordeaux (France). Nous avons montré que la plupart des Anaprops sont caractérisés par un spectre monofractal contrairement aux échos de précipitations qui présentent un caractère multi fractal. En outre, nous avons montré que le coefficient d'Holder ou la combinaison mode du spectre et densité de squelette par pixel se présentent comme des facteurs robustes de discrimination entre les deux types d'échos. En effet, les échos parasites sont pratiquement éliminés à 98% alors que les échos de précipitation sont quasiment conservés à 98,2%. Aussi, nous avons montré que l'erreur entre l'intensité mesurée au sol et estimée après traitement des échos parasites ne dépasse pas 5% pour le site de Sétif. Etant donné que le temps de traitement est égal à trois minutes, les images radar peuvent être traitées en temps réel

RGB Medical Video Compression Using Geometric Wavelet

The video compression is used in a wide of applications from medical domain especially in telemedicine. Compared to the classical transforms, wavelet transform has significantly better performance in horizontal, vertical and diagonal directions. Therefore, this transform introduces high discontinuities in complex geometrics. However, to detect complex geometrics is one key challenge for the high efficient compression. In order to capture anisotropic regularity along various curves a new efficient and precise transform termed by bandelet basis, based on DWT, quadtree decomposition and optical flow is proposed in this paper. To encode significant coefficients we use efficient coder SPIHT. The experimental results show that the proposed algorithm DBT-SPIHT for low bit rate (0.3Mbps) is able to reduce up to 37.19% and 28.20% of the complex geometrics detection compared to the DWT-SPIHT and DCuT-SPIHT algorithm

Deep learning based face beauty prediction via dynamic robust losses and ensemble regression

In the last decade, several studies have shown that facial attractiveness can be learned by machines. In this paper, we address Facial Beauty Prediction from static images. The paper contains three main contributions. First, we propose a two-branch architecture (REX-INCEP) based on merging the architecture of two already trained networks to deal with the complicated high-level features associated with the FBP problem. Second, we introduce the use of a dynamic law to control the behaviour of the following robust loss functions during training: ParamSmoothL1, Huber and Tukey. Third, we propose an ensemble regression based on Convolutional Neural Networks (CNNs). In this ensemble, we use both the basic networks and our proposed network (REX-INCEP). The proposed individual CNN regressors are trained with different loss functions, namely MSE, dynamic ParamSmoothL1, dynamic Huber and dynamic Tukey. Our approach is evaluated on the SCUT-FBP5500 database using the two evaluation scenarios provided by the database creators: 60%-40% split and five-fold cross-validation. In both evaluation scenarios, our approach outperforms the state of the art on several metrics. These comparisons highlight the effectiveness of the proposed solutions for FBP. They also show that the proposed dynamic robust losses lead to more flexible and accurate estimators.This work was partially funded by the University of the Basque Country , GUI19/027

A High-Performance System Architecture for Medical Imaging

Medical imaging is classified into different modalities such as ultrasound, X-ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), single-photon emission tomography (SPECT), nuclear medicine (NM), mammography, and fluoroscopy. Medical imaging includes various imaging diagnostic and treatment techniques and methods to model the human body, and therefore, performs an essential role to improve the health care of the community. Medical imaging, scans (such as X-Ray, CT, etc.) are essential in a variety of medical health-care environments. With the enhanced health-care management and increase in availability of medical imaging equipment, the number of global imaging-based systems is growing. Effective, safe, and high-quality imaging is essential for the medical decision-making. In this chapter, we proposed a medical imaging-based high-performance hardware architecture and software programming toolkit called high-performance medical imaging system (HPMIS). The HPMIS can perform medical image registration, storage, and processing in hardware with the support of C/C++ function calls. The system is easy to program and gives high performance to different medical imaging applications

Pain Analysis using Adaptive Hierarchical Spatiotemporal Dynamic Imaging

Automatic pain intensity estimation plays a pivotal role in healthcare and medical fields. While many methods have been developed to gauge human pain using behavioral or physiological indicators, facial expressions have emerged as a prominent tool for this purpose. Nevertheless, the dependence on labeled data for these techniques often renders them expensive and time-consuming. To tackle this, we introduce the Adaptive Hierarchical Spatio-temporal Dynamic Image (AHDI) technique. AHDI encodes spatiotemporal changes in facial videos into a singular RGB image, permitting the application of simpler 2D deep models for video representation. Within this framework, we employ a residual network to derive generalized facial representations. These representations are optimized for two tasks: estimating pain intensity and differentiating between genuine and simulated pain expressions. For the former, a regression model is trained using the extracted representations, while for the latter, a binary classifier identifies genuine versus feigned pain displays. Testing our method on two widely-used pain datasets, we observed encouraging results for both tasks. On the UNBC database, we achieved an MSE of 0.27 outperforming the SOTA which had an MSE of 0.40. On the BioVid dataset, our model achieved an accuracy of 89.76%, which is an improvement of 5.37% over the SOTA accuracy. Most notably, for distinguishing genuine from simulated pain, our accuracy stands at 94.03%, marking a substantial improvement of 8.98%. Our methodology not only minimizes the need for extensive labeled data but also augments the precision of pain evaluations, facilitating superior pain management

D-TrAttUnet: Dual-Decoder Transformer-Based Attention Unet Architecture for Binary and Multi-classes Covid-19 Infection Segmentation

In the last three years, the world has been facing a global crisis caused by Covid-19 pandemic. Medical imaging has been playing a crucial role in the fighting against this disease and saving the human lives. Indeed, CT-scans has proved their efficiency in diagnosing, detecting, and following-up the Covid-19 infection. In this paper, we propose a new Transformer-CNN based approach for Covid-19 infection segmentation from the CT slices. The proposed D-TrAttUnet architecture has an Encoder-Decoder structure, where compound Transformer-CNN encoder and Dual-Decoders are proposed. The Transformer-CNN encoder is built using Transformer layers, UpResBlocks, ResBlocks and max-pooling layers. The Dual-Decoder consists of two identical CNN decoders with attention gates. The two decoders are used to segment the infection and the lung regions simultaneously and the losses of the two tasks are joined. The proposed D-TrAttUnet architecture is evaluated for both Binary and Multi-classes Covid-19 infection segmentation. The experimental results prove the efficiency of the proposed approach to deal with the complexity of Covid-19 segmentation task from limited data. Furthermore, D-TrAttUnet architecture outperforms three baseline CNN segmentation architectures (Unet, AttUnet and Unet++) and three state-of-the-art architectures (AnamNet, SCOATNet and CopleNet), in both Binary and Mutli-classes segmentation tasks