Parisa Gifani

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Noise suppression of echocardiography images is a challenging issue for accurate and effective human interpretation and computer-assisted analysis. In spite of comprehensive speckle reduction methods, until now there have been few studies of denoising echocardiography sequences based on temporal information. In this article, a fast and accurate filter based(More)
The electroencephalogram (EEG) is the brain signal containing valuable information about the normal or epileptic state of the brain. In this paper a discrete wavelet-spectral entropy (SEN) method is presented for epileptic seizures detection through the analysis of EEGs and EEG sub-bands. The EEG signal is decomposed by discrete wavelet transform into its(More)
Medical applications of ultrasound imaging have expanded enormously over the last two decades. De-noising is challenging issues for better medical interpretation and diagnosis on high volume of data sets in echocardiography. In this paper, manifold learning algorithm is applied on 2-D echocardiography images to discover the relationship between the frames(More)
The depth of anesthesia quantification has been one of the most research interests in the field of EEG signal processing and nonlinear dynamical analysis has emerged as a novel method for the study of complex systems in the past few decades. In this investigation we use the concept of nonlinear time series analysis techniques to reconstruct the attractor of(More)
The depth of anesthesia estimation has been one of the most research interests in the field of EEG signal processing in recent decades. In this paper we present a new methodology to quantify the depth of anesthesia by quantifying the dynamic fluctuation of the EEG signal. Extraction of useful information about the nonlinear dynamic of the brain during(More)
The automatic detection of end-diastole and end-systole frames of echocardiography images is the first step for calculation of the ejection fraction, stroke volume and some other features related to heart motion abnormalities. In this paper, the manifold learning algorithm is applied on 2D echocardiography images to find out the relationship between the(More)
Steady increase in the number of patients with myocardial infarction or cerebral infarction, both of which are considered to be mainly caused by atherosclerosis, is becoming a serious problem. Non-invasive measurement of the mechanical properties of the vessel wall, such as vibratility, is useful for the diagnosis of atherosclerosis, since there are(More)
By development of ultrasound technology, ultrasonic imaging has become a frequently used diagnostic tool. The main benefit for this system is real-time imaging and being safe compared to other methods of imaging like Magnetic Resonance Imaging (MRI). A challenging issue for ultrasonic imaging is low quality due to naturally inherited speckle noise. In this(More)
Increasing frame rate is a challenging issue for better interpretation of medical images and diagnosis based on tracking the small transient motions of myocardium and valves in real time visualization. In this paper, manifold learning algorithm is applied to extract the nonlinear embedded information about echocardiography images from the consecutive images(More)