Seuillage d'images basé sur l'opérateur de Teager-Kaiser

Le seuillage de l'histogramme d'une image est une procédure classique de segmentation. Dans ce travail, on s'intéresse au problème du seuillage d'images en deux niveaux, pour extraire une information binaire pertinente. On propose une nouvelle méthode de seuillage qui inclut l'information contextuelle du pixel, en utilisant les contours pour estimer le seuil. Plus exactement, au lieu d'utiliser un détecteur de contours classique, on utilise l'Opérateur d'Énergie de Teager-Kaiser (OETK) pour mesurer l'activité en énergie du pixel. En effet, cet opérateur reflète mieux l'activité en énergie du pixel qu'un détecteur de contours classique (Laplacien,...). Les résultats obtenus sur des images synthétiques et réelles montrent l'intérêt de l'attribut du contexte. La comparaison avec les méthodes d'Otsu et de Kitler montrent l'apport de l'OETK à l'estimation du seuil

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant Number: NSC102-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science and Technology in Taiwan (Grant Numbers: CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant Number: 51475342)

Débruitage des signaux par approche EMD : Multi-EMDSG

Le but de ce travail est de mettre en œuvre la Décomposition Modale Empirique (EMD) [1] dans une problématique de débruitage des signaux via une nouvelle approche appelée Multi-EMDSG, combinant l'EMD et le filtre polynomial de Savitzky-Golay (SG) [2]. Pour cela, on exploite les caractéristiques des modes empiriques issus de l'EMD pour étudier une nouvelle approche de débruitage des signaux. On montre, sur la base de simulations intensives, que l'application itérative du même processus EMDSG proposé dans [3]: (EMD ⇒ débruitage par filtre de Savitzky-Golay (SG) ⇒ reconstruction du signal) permet d'améliorer sensiblement les résultats du débruitage. Ainsi, l'EMD associée à un filtrage et appliquée d'une manière itérative permet d'améliorer le Rapport Signal à Bruit (RSB) comparé à l'approche ondelettes. L'influence de certains paramètres sont étudiés (taille de la fenêtre de filtrage...) aussi bien pour l'approche EMDSG que pour son extension Multi-EMDSG. Enfin, l'approche Multi-EMDSG est comparée aux approches EMDSG et ondelettes

Voiced speech enhancement based on adaptive filtering of selected intrinsic mode functions

In this paper a new method for voiced speech enhancement combining the Empirical Mode Decomposition (EMD) and the Adaptive Center Weighted Average (ACWA) filter is introduced. Noisy signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). Since voiced speech structure is mostly distributed on both medium and low frequencies, the shorter scale IMFs of the noisy signal are beneath noise, however the longer scale ones are less noisy. Therefore, the main idea of the proposed approach is to only filter the shorter scale IMFs, and to keep the longer scale ones unchanged. In fact, the filtering of longer scale IMFs will introduce distortion rather than reducing noise. The denoising method is applied to several voiced speech signals with different noise levels and the results are compared with wavelet approach, ACWA filter and EMD–ACWA (filtering of all IMFs using ACWA filter). Relying on exhaustive simulations, we show the efficiency of the proposed method for reducing noise and its superiority over other denoising methods, i.e., to improve Signal-to-Noise Ratio (SNR), and to offer better listening quality based on a Perceptual Evaluation of Speech Quality (PESQ). The present study is limited to signals corrupted by additive white Gaussian noise.In this paper a new method for voiced speech enhancement combining the Empirical Mode Decomposition (EMD) and the Adaptive Center Weighted Average (ACWA) filter is introduced. Noisy signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). Since voiced speech structure is mostly distributed on both medium and low frequencies, the shorter scale IMFs of the noisy signal are beneath noise, however the longer scale ones are less noisy. Therefore, the main idea of the proposed approach is to only filter the shorter scale IMFs, and to keep the longer scale ones unchanged. In fact, the filtering of longer scale IMFs will introduce distortion rather than reducing noise. The denoising method is applied to several voiced speech signals with different noise levels and the results are compared with wavelet approach, ACWA filter and EMD–ACWA (filtering of all IMFs using ACWA filter). Relying on exhaustive simulations, we show the efficiency of the proposed method for reducing noise and its superiority over other denoising methods, i.e., to improve Signal-to-Noise Ratio (SNR), and to offer better listening quality based on a Perceptual Evaluation of Speech Quality (PESQ). The present study is limited to signals corrupted by additive white Gaussian noise