nils-herrmann/clean_scirepeval huggingface dataset:
Minimizing BER in DFE's with the Adatron algorithm

In this paper we apply the Structural Risk Minimization (SRM) principle to minimize the Bit Error Rate in Decision Feedback Equalizers (DFE). We consider both linear discriminant (Optimal Hyperplane) and nonlinear discriminant (Support Vector Machine) classifiers as an alternative to the linear MMSE-DFE and Radial Basis Function (RBF) networks, respectively. A fast and simple adaptive algorithm called the Adatron is applied to obtain the linear or nonlinear classifier. In this way we avoid the high computational cost of quadratic programming. We also study the performance of soft margin classifiers: it is shown that to consider a regularized problem improves the BER, mainly at low SNR's. Furthermore, an adaptive implementation is discussed. Some simulation examples show the advantages of the proposed linear (OH) and nonlinear (SVM) DFE's: a better performance in comparison to the linear MMSE-DFE and a simpler structure in comparison to the RBF-DFE.
Polar Run-Length Features in Segmentation of Retinal Blood Vessels

Manual segmentation of retinal blood vessels in optic fundus images is a tiresome task. Several methods have previously been proposed for the automatic segmentation of retinal blood vessels. In this paper we propose a classifier-based method. First the images are preprocessed so that the within class variability of the vessel and background classes are minimized. Next, the image is scanned with a window of a certain size. Polar run-length matrices are simply created by transforming the windows into polar coordinates and then constructing conventional run length matrices. Two features are then extracted for each gray level value in the polar run length matrix. The feature vectors are then classified using a multilayer perceptron artificial neural network. The performance of the proposed method is compared with that of the human observers and with those methods previously reported in literature.
Ball Detection with the Aim of Corner Event Detection in Soccer Video

In the present work, automatic corner detection in soccer games based on image features (e.g., object-based features) has been studied. For this purpose, a framework has been proposed that consists of five steps. This paper mainly focuses on the first three steps and specially ball detection step. Ball position on the field plays an important role in determining which event has occurred in the game. Therefore, it is necessary to detect exact position of the ball in the playfield and then track it. Ball trajectory that can be obtained via tracking is useful for identifying and detecting main events of soccer games. In these three steps, the most important processing that has been applied to the images is based on image segmentation to detect playfield, field lines, and ball. Cleaning morphological method is applied to detect the ball. This method is real-time and automatic, it yields superior results in comparison with other common methods such as Template Matching and Circular Hough Transform (CHT). By applying the proposed method, only one candidate for the ball position is obtained and non-ball candidates are removed, hence, it is more reliable than other methods. The results of the proposed method are compared with those of CHT. They illustrate that the proposed method is fast, effective, and reliable.
Feature-based approach to fuse fMRI and DTI in epilepsy using joint independent component analysis

Functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) provide complementary information. Signal processing and statistical models may be used to fuse neuroimaging data across different imaging modalities. In this paper, we present a data driven method for fusing resting state fMRI and diffusion tensor imaging (DTI) data at feature level. The features are amplitude of low frequency fluctuations (ALFF) and fractional anisotropy (FA) extracted from fMRI and DTI datasets of epilepsy and healthy controls, respectively. We discuss main issues associated with group independent component analysis (ICA) as a fusion method. We address our proposed approach for combining two modalities across subjects and back reconstruction of independent components for each group and each subject. Our results indicate that connectivity of regions in default mode network depends on integrity of white matter that connects the two hemispheres (corpus callosum). The proposed signal processing and statistical methods facilitate evaluation of brain connectivity using different modalities. Separate analysis of data modalities does not reveal results of joint analysis.
Optimal transformation for correcting partial volume averaging effects in magnetic resonance imaging

Segmentation of a feature of interest while correcting for partial volume averaging effects is a major tool for identification of hidden abnormalities, fast and accurate volume calculation, and 3-D visualization in the field of magnetic resonance imaging (MRI). The authors present the optimal transformation for simultaneous segmentation of a desired feature and correction of partial volume averaging effects (CPV), while maximizing of the signal-to-noise ratio of the desired feature. It is proved that CPV requires the removal of the interfering features from the scene. It is also proved that CPV can be achieved merely by a linear transformation. It is finally shown that the optimal transformation matrix is easily obtained using the Gram-Schmidt orthogonalization procedure, which is numerically stable.<<ETX>>
Soft computing approaches to computer aided decision making for temporal lobe epilepsy

This paper presents and compares soft computing approaches for prediction of surgery outcome in temporal lobe epilepsy. Because of a wide range of effective parameters in epilepsy and unclear exact contribution of each, determination of the best treatment is difficult. We have implemented and compared data fusion methods and decision support algorithms to overcome this difficulty. Our simulation studies and experimental results using HBIDS (human brain image database system) data show the power of LS-SVM (least squared support vector machine) classifiers for this purpose.
RECOVERY LIMITATIONS OF MEG SOURCE LOCALIZATION MODEL FOR EPILEPSY

Source localization in magnetoencephalography (MEG) has received a lot of attention in the recent years. MEG inverse problem is an ill-posed problem in from of Bntimesl=GntimesmJmtimesl, where B is the vector of magnetic fields measured by the magnetic sensors, G is the lead field matrix, and J is the vector of source current in the brain. In practice, nLtm and J is usually a sparse vector. In this paper, we employ a complete reconstruction criterion to estimate the maximum number of active sources in the model or equivalently the volume of the active areas that guarantees a stable solution to the MEG inverse problem. Based on some prior information about the volume of the active areas, we then evaluate the stability of the solution with the employed model. Our approach guarantees an error upper bound on the solution with a certain number of active points. In the experiments, we have demonstrated the technique using some simulations.
Robust vehicle tracking algorithm for nighttime videos captured by fixed cameras in highly reflective environments

In this study, the problem of vehicle detection, tracking and speed estimation in the nighttime traffic surveillance videos captured in highly reflective environments is considered. In this case, a robust algorithm is proposed which uses vehicle headlights as their prominent features. The proposed algorithm consists of three main stages. In the first stage, bright objects are segmented by thresholding the grey-scale image. An effective algorithm is then applied to distinguish between vehicles lights and lights reflected on the road and on the vehicles bodies. In the second stage, the segmented bright objects are tracked using their spatial characteristics and their shapes and then, their speeds are estimated. To correct the camera perspective effect and reduce computational complexity, a projective transformation is used. In the third stage, the lights of each vehicle are grouped and paired using their positions and speeds. Motorbikes are also identified among the unpaired lights in this stage. Finally, the proposed real-time system is implemented in C and applied to videos captured by traffic surveillance cameras in some highways in Iran. Experimental results reveal that accuracy of the algorithm proposed for vehicle detection is more than 98%.
4D wavelet noise suppression of MR diffusion tensor data

Diffusion tensor imaging (DTI) is known to be promising for providing anatomical information about white-matter fiber bundles that cannot be obtained by other non-invasive in vivo imaging methods. However, its application is limited because of its low signal-to-noise ratio and significant imaging artifacts. To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging 4D wavelet denoising technique is used to improve the signal to noise ratio (SNR) of diffusion tensor images. To evaluate the proposed method, a high SNR data set is built by repeating and averaging the data acquisition several times and is compared to the denoised data. Our results revealed that wavelets would effectively reduce the noise in DTI data with less blurring of tissue types, especially in the white matter. It would suggest that by using the 4D wavelet noise suppression, one could decrease the acquisition time and still have an acceptable SNR.
Discriminative features for interictal epileptic discharges in intracerebral EEG signals

This paper extracts features and selects the most discriminate feature subset for classifying interictal epileptic discharge periods (IED) from non-IED periods in intracerebral EEG (iEEG) signals. Generalized autoregressive conditional heteroscedasticity (GARCH) model based on the student t-distribution is used to describe the wavelet coefficients of the iEEG signals. A variety of features are extracted from the coefficients of GARCH models. The Markov random field (MRF) based feature subset selection method is used to select the most discriminative features. Experimental results on real patients' data validate the effectiveness of the selected features.
Compressive sensing cardiac cine MRI using invertible non-linear transform

Since compressive sensing (CS) is robust to noise and does not require any special hardware, CS-based methods are advantageous to other ones for increasing MRI speed. Monotonic re-ordering (sorting) of the desired signal can increase its sparsity and consequently CS reconstruction efficiency. To use this, it is essential to estimate re-ordering operator which usually has been done by parallel imaging methods. These methods need regular sampling patterns which may not be optimum for CS reconstruction. We replaced the re-ordering operator by an invertible non-linear transform. The advantages of this approach are greater reconstruction speed, no need for preliminary reconstruction and estimation of the re-ordering operator and possibility of having an arbitrary sampling pattern. We successfully applied our approach to speed up cardiac cine MRI by factor of 2. This reduced the mean reconstruction error from 5.65% for standard CS to 5.41% and reconstruction time from 66.3 for CS plus re-ordering operator to 5.1 seconds.
Semi-automatic epilepsy spike detection from EEG signal using Genetic Algorithm and Wavelet transform

A novel algorithm is proposed for identifying epileptic features in electroencephalograph (EEG) signals automatically. The proposed algorithm is based on the combination of the Genetic Algorithm (GA) and the Wavelet transform. Optimal Wavelet basis functions that adapt the spikes of the EEG signal are first designed using GA. Then they are used as matched filters to identify the spikes related to seizure activity from the EEG recordings using Wavelet transform and a threshold-based estimation method. The method can estimate the number and the location of epileptic spikes in an EEG signal very fast and almost in real time. Hence, it is suitable for data mining of EEG recordings of epileptic patients for fundamental studies of epilepsy, prediction of seizures, and treatment of epilepsy. We have applied and evaluated the method using different samples of real clinical EEG data of epileptic patients, where it has shown a very high sensitivity (more than 90%) and selectivity (more than 90%).
Network based analysis of cognitive related resting state networks in Alzheimer's disease

Alzheimer's disease (AD) is accompanied by a widespread disruption of neural pathways. This in turn leads to alterations in functional brain networks and a progressive decline in cognitive functions. Despite the success of previous studies in detecting topological alterations in the AD brain, few studies have investigated networks that are related to decline of different cognitive functions in AD, with regard to disconnections and compensatory mechanisms. In this study, we adopted network based statistics (NBS) and resting state functional magnetic resonance imaging (rs-fMRI) in order to find alterations in the functional network and explore networks that are correlated to decline in four cognitive domains. To this end, NBS was applied to the rs-fMRI data and four Montreal cognitive assessment sub-scores of 30 AD patients and 34 healthy subjects. Our results suggest the loss of connections between regions of frontal lobe and two other lobes of temporal and parietal. Moreover, cognitive deficits are in line with changes in the resting state network such as thinning effect and disconnection.
ARMA modeling for estimation of permeability from perfusion MRI

We develop noninvasive MRI techniques that quantify the permeability of the Blood-Brain Barrier (BBB). Using such gadolinium compounds as Gd-DTPA and gadomer-17, changes in R1 (R1=1/T1) can be measured and used as estimates of tissue concentration versus time, thus permitting estimates of BBB permeability parameters. Estimating BBB permeability parameters requires deconvolution in a linear system, a task that has been accomplished heretofore by nonlinear least-squares procedures, although these techniques tend to instability in the presence of noise. We introduce a method using Z-transform and Autoregressive Moving Average (ARMA) modeling to perform this deconvolution. This method has the advantage that it linearizes the optimization procedure by which parametric estimates are formed, and it stabilizes the deconvolution. in the presence of noise.
Activation detection in multi-subject studies of fMRI using GLRT

A new method based on generalized likelihood ratio test (GLRT) for activation detection in multi-subject studies of functional MRI (fMRI) is proposed. In this method, we test the correlation between the fMRI time series of different subjects and the bases of a signal subspace which increases the flexibility of method in detecting different shapes of hemodynamic response. The proposed multivariate method can be applied to group studies where the conventional cross-correlation method cannot be used due to its univariate property. This method is applied to both experimental and simulated fMRI data and the results are compared to those of general linear model (GLM). We show that the proposed method detects more significant activated regions in analyzing experimental data and more true voxels in simulated data
Atlas based segmentation of white matter fiber bundles in DTMRI using fractional anisotropy and principal eigen vectors

In this work, we develop an atlas based method for automatic segmentation of white matter fiber bundles. To this end, we propose a new method for registration of diffusion tensor (DT) images using DTI information which is also used in the fiber tracking process, and we also propose a strategy for segmenting the fiber bundles using the new registration method and a probabilistic white matter atlas. We apply the registration method to 13 real DTI data sets and evaluate the results by comparing the level of alignment of all fibers. Then, we use the proposed strategy to segment 10 major fiber bundles in one of the subjects. One of the advantages of such a method is the robustness of the results thanks to using prior knowledge. The segmented results can be used for comparing and evaluating other fiber bundle segmentation methods.
Voxel based treatment prediction using diffusion anisotropy indices and spatial information in Glioblastoma Multiform tumor

In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance Images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pre-treatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures.
A discrete curvature-based deformable surface model with application to segmentation of volumetric images

In this paper, we present a new curvature-based three-dimensional (3-D) deformable surface model. The model deforms under defined force terms. Internal forces are calculated from local model curvature, using a robust method by a least-squares error (LSE) approximation to the Dupin indicatrix. External forces are calculated by applying a step expansion and restoration filter (SEF) to the image data. A solution for one of the most common problems associated with deformable models, self-cutting, has been proposed in this work. We use a principal axis analysis and reslicing of the deformable model, followed by triangulation of the slices, to remedy self-cutting. We use vertex resampling, multiresolution deformation, and refinement of the mesh grid to improve the quality of the model deformation, which leads to better results. Examples of the model application to different cases (simulation, magnetic resonance imaging (MRI), computerized tomography (CT), and ultrasound images) are presented, showing diversity and flexibility of the model.
Automatic organs' detection in WCE

Wireless capsule endoscopy (WCE) views the entire gastrointestinal (GI) tract. A main problem associated with this novel device is that too many frames must be reviewed by physicians. Thus it is essential to find an automatic and intelligent method to help physicians. One of the problems in WCE is its difficulty to distinguish among different organ's tissues. So, we introduce two novel algorithms which are able to classify main organs (among esophagus, stomach, small bowel and colon) in WCE's frames. In order to obtain our aim, we use statistic features (Haralick features) and non-statistic features (different diagrams and Gabor filter banks), colored features and non-colored features. Our experimental studies indicate good results that are shown in conclusion.
Spatiotemporal fMRI data processing using generalized canonical correlation analysis

Common fMRI data processing techniques usually minimize a temporal cost function or fit a temporal model to extract an activity map. In our previous work  we used generalized canonical correlation analysis to extract a highly, spatially reproducible statistical parametric map (SPM) from fMRI data using a cost function that does not depend on a model of the subjects' temporal response. Here we focus on using a cost function that simultaneously maximizes temporal and spatial reproducibility of MRI statistical parametric map. Based on a modified version of generalized canonical correlation analysis (gCCA) we propose a method to extract a highly reproducible map by maximizing the sum of the pair-wise correlations between pairs of maps while the associated temporal response to the extracted map follows the subjects' temporal response. The proposed method is applied to BOLD fMRI datasets without any spatial smoothing from 10 subjects performing a simple reaction time (RT) task. Using the NPAIRS split-half resampling framework with a reproducibility measure based on SPM correlations  we compare the proposed approach with our previous work presented in . Our results show that the proposed modified gCCA is an efficient approach for extracting both default mode network and task mode network.
Cloud-based deep learning of big EEG data for epileptic seizure prediction

Developing a Brain-Computer Interface (BCI) for seizure prediction can help epileptic patients have a better quality of life. However, there are many difficulties and challenges in developing such a system as a real-life support for patients. Because of the nonstationary nature of EEG signals, normal and seizure patterns vary across different patients. Thus, finding a group of manually extracted features for the prediction task is not practical. Moreover, when using implanted electrodes for brain recording massive amounts of data are produced. This big data calls for the need for safe storage and high computational resources for real-time processing. To address these challenges, a cloud-based BCI system for the analysis of this big EEG data is presented. First, a dimensionality-reduction technique is developed to increase classification accuracy as well as to decrease the communication bandwidth and computation time. Second, following a deep-learning approach, a stacked autoencoder is trained in two steps for unsupervised feature extraction and classification. Third, a cloud-computing solution is proposed for real-time analysis of big EEG data. The results on a benchmark clinical dataset illustrate the superiority of the proposed patient-specific BCI as an alternative method and its expected usefulness in real-life support of epilepsy patients.
NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL IMAGE SEGMENTATION

We propose a 3D nonparametric, entropy-based, coupled, multishape approach for the segmentation of subcortical brain structures in magnetic resonance images (MRI). Our method uses PCA to capture structures variability. Because of complex relationships of pose and shape of the coupled structures, we only use their shape and size relation. To this end, we apply separate registrations of the structures. For each structure, we consider a similarity transform using seven parameters. In addition, to generate most accurate results, we estimate probability density functions (pdf) iteratively. The proposed method minimizes an entropy-based energy function using quasi-Newton algorithm. To improve the results, we use analytical derivatives. Sample results are given for the segmentation of putamen, thalamus and caudate illustrating the impact of coupling on the accuracy of the results.