A Sparse Analysis-Based Single Image Super-Resolution

In the current study, we were inspired by sparse analysis signal representation theory to propose a novel single-image super-resolution method termed “sparse analysis-based super resolution” (SASR). This study presents and demonstrates mapping between low and high resolution images using a coupled sparse analysis operator learning method to reconstruct high resolution (HR) images. We further show that the proposed method selects more informative high and low resolution (LR) learning patches based on image texture complexity to train high and low resolution operators more efficiently. The coupled high and low resolution operators are used for high resolution image reconstruction at a low computational complexity cost. The experimental results for quantitative criteria peak signal to noise ratio (PSNR), root mean square error (RMSE), structural similarity index (SSIM) and elapsed time, human observation as a qualitative measure, and computational complexity verify the improvements offered by the proposed SASR algorithm

Extraction of BIS™ index sub-parameters in different anesthetic and sedative levels

Monitoring the depth of anesthesia is important to prevent undesirable events during surgery. According to direct effect of anesthetic drugs on synaptic activity of neurons and after presentation of anesthesia depth monitor (BIS) in 1996, there was a great interest on electroencephalogram analysis to investigate depth of anesthesia. Now there are large numbers of methods and algorithms in this field and every new method is compared with BIS index. BIS algorithm is based on three sub-parameters including time, frequency and higher order statistics domain parameters but the detailed algorithm is not in the public domain. In this paper, proper methods are presented for calculating three sub-parameters. Results of applying these methods to collected clinical data are presented. Efficiency of these methods will be evaluated based on appropriate statistical analysis

Vesselness-guided Active Contour: A Coronary Vessel Extraction Method

Vessel extraction is a critical task in clinical practice. In this paper, we propose a new approach for vessel extraction using an active contour model by defining a novel vesselness-based term, based on accurate analysis of the vessel structure in the image. To achieve the novel term, a simple and fast directional filter bank is proposed, which does not employ down sampling and resampling used in earlier versions of directional filter banks. The proposed model not only preserves the performance of the existing models on images with intensity inhomogeneity, but also overcomes their inability both to segment low contrast vessels and to omit non-vessel structures. Experimental results for synthetic images and coronary X-ray angiograms show desirable performance of our model

Analysis of the Behavior of a Seizure Neural Mass Model Using Describing Functions

Neural mass models are computational nonlinear models that simulate the activity of a population of neurons as an average neuron, in such a way that different inhibitory post-synaptic potential and excitatory post-synaptic potential signals could be reproduced. These models have been developed either to simulate the recognized neural mechanisms or to predict some physiological facts that are not easy to realize naturally. The role of the excitatory and inhibitory activity variation in seizure genesis has been proved, but it is not evident how these activities influence appearance of seizure like signals. In this paper a population model is considered in which the physiological inter-relation of the pyramidal and inter-neurons of the hippocampus has been appropriately modeled. The average neurons of this model have been assumed to act as a linear filter followed by a nonlinear function. By changing the gain of excitatory and inhibitory activities that are modeled by the gain of the filters, seizure-like signals could be generated. In this paper through the analysis of this nonlinear model by means of the describing function concepts, it is theoretically shown that not only the gains of the excitatory and inhibitory activities, but also the time constants may play an efficient role in seizure genesis

Relationship between consciousness and electrical activity of brain neurons in patients undergoing aortic valve replacement surgery

Background & Objective: Monitoring the depth of anesthesia is very important to prevent undesirable events during surgery, such as intra operative awareness and overdosing. It is shown that anesthetic agents have direct effects on synaptic activity of brain neurons. So there is a great interest on electroencephalogram analysis as a depth of anesthesia estimator. Due to difficulties in visual explanation of EEG, automatic and computer based signal processing methods have been used to assess the depth of anesthesia. Investigating the relationship between conscious level of patients and electrical activity of brain neurons was the main aim of this study. Materials & Methods: In this study, EEG signals of six patients undergoing aortic valve replacement surgery have been acquired and recorded in a computer. After applying signal processing methods to these data, 3 different measures included temporal, spectral and bispectral parameters have been extracted. Mean values of mentioned parameters in different anesthetic regimens and levels have been analyzed by ANOVA in SPSS software. Results: Extracted temporal parameter is correlated with depth of anesthesia in deep anesthetic levels and spectral one is correlated with depth of anesthesia in moderate and light levels (P<0.05). Bispectral parameter is correlated with the depth of anesthesia only in ICU (P<0.05). Conclusion: Findings of this study confirm the relationship between consciousness and electrical activity of brain neurons and recommend the use of EEG processing techniques to monitor, control and estimate the depth of anesthesia in operating room and ICU ward