nils-herrmann/clean_scirepeval huggingface dataset:
Single channel speech separation with a frame-based pitch range estimation method in modulation frequency

Computational Auditory Scene Analysis (CASA) has attracted a lot of interest in segregating speech from monaural mixtures. In this paper, we propose a new method for single channel speech separation with frame-based pitch range estimation in modulation frequency domain. This range is estimated in each frame of modulation spectrum of speech by analyzing onsets and offsets. In the proposed method, target speaker is separated from interfering speaker by filtering the mixture signal with a mask extracted from the modulation spectrogram of mixture signal. Systematic evaluation shows an acceptable level of separation comparing with classic methods.
Nonlinear Granger Causality using ANFIS for identification of causal couplings among EEG/MEG time series

Identifying the causal couplings among EEG/MEG time series is an important problem in the neuroscience field. For linear stochastic models, Granger causality (GC) is used as a simple concept to explore such interactions. In this paper, we extend GC concept to a nonlinear version based on the Adaptive Neuro Fuzzy Inference System (ANFIS) and propose a new effective connectivity measure (ANFISGC) with capability in detecting linear and nonlinear causal information flow between time series. We applied the proposed method to the simulated datasets and compared its performance with the classic Linear Granger Causality (LGC). In a linear (AR) simulation model, LGC performs the same as ANFISGC but in the case of nonlinear models, ANFISGC outperforms LGC.
Application of MEG coherence in lateralization of mTLE.

Magnetoencephalography (MEG) is a noninvasive imaging method for localization of focal epileptiform activity in patients with epilepsy. This study investigates the cerebral functional abnormalities quantified by MEG coherence laterality in mesial temporal lobe epilepsy (mTLE). Resting state MEG data was analyzed using MEG coherence source imaging (MEG-CSI) method to determine the coherence in 54 anatomical sites in 12 adult mTLE patients and 12 age- and gender-matched controls. MEG coherence laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in insular cortex and both lateral orbitofrontal and superior temporal gyri (p<;0.025). None of these anatomical sites showed statistically significant differences in coherence laterality between right and left sides of controls. Coherence laterality was in agreement with the declared side of epileptogenicity in insular cortex (in 75% of patients) and both lateral orbitofrontal (83%) and superior temporal gyri (84%). Combining all significant laterality indices improved the lateralization accuracy to 92%. The proposed methodology for using MEG to investigate the abnormalities related to focal epileptogenicity and propagation can provide a further means of noninvasive lateralization.
Consensus Feature Ranking in Datasets with Missing Values

Development of a feature ranking method based upon the discriminative power of features and unbiased towards classifiers is of interest. We have studied a consensus feature ranking method, based on multiple classifiers, and have shown its superiority to well known statistical ranking methods. In a target environment such as a medical dataset, missing values and an unbalanced distribution of data must be taken into consideration in the ranking and evaluation phases in order to legitimately apply a feature ranking method. In a comparison study, a Performance Index (PI) is proposed that takes into account both the number of features and the number of samples involved in the classification.
Activation detection in fMRI using a maximum energy ratio statistic obtained by adaptive spatial filtering

An adaptive spatial filtering method is proposed that takes into account contextual information in fMRI activation detection. This filter replaces the time series of each voxel with a weighted average of time series of a small neighborhood around it. The filter coefficients at each voxel are derived so as to maximize a test statistic designed to indicate the presence of activation. This statistic is the ratio of the energy of the filtered time series in a signal subspace to the energy of the residuals. It is shown that the filter coefficients and the maximum energy ratio can be found through a generalized eigenproblem. This approach equates the filter coefficients to the elements of an eigenvector corresponding to the largest eigenvalue of a specific matrix, while the largest eigenvalue itself becomes the maximum energy ratio that can be used as a statistic for detecting activation. The distribution of this statistic under the null hypothesis is derived by a nonparametric permutation technique in the wavelet domain. Also, in this paper we introduce a new set of basis vectors that define the signal subspace. The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis. This approach circumvents the need for a priori assumptions about the exact shape of the HRF. Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value. Analysis of simulated data and motor task fMRI datasets from six volunteers using the method proposed here showed an improved sensitivity as compared to a conventional test with a similar statistic applied to spatially smoothed data.
Face recognition: A Sparse Representation-based Classification using Independent Component Analysis

In this paper, we will describe a new method based on Sparse Representation-based Classification (SRC) for face recognition. We have used histogram equalization as a preprocessing method in order to overcome the illumination variation problem. Using Independent Component Analysis we have obtained a feature vector for each face image which is robust to illumination variations and occlusion. Although SRC is robust against occlusion, it is rather slow. By using features with smaller dimensions but enough information, we can obtain better recognition rates in shorter periods. This method was tested on Extended Yale B database and obtained the recognition rates of 98.51% and 95.77% in presence of 10% and 20% occlusion, respectively.
A 3-D deformable surface method for automatic hippocampus-amygdala complex segmentation

In this paper, we propose an atlas-based method for hippocampus-amygdala complex segmentation. An atlas is registered on all subjects and its transformation is calculated for each subject. This transformation is applied to the structural segmentation of the complex in atlas to construct an initial surface for the hippocampus-amygdala complex of each subject. A possibility approach is introduced for the segmentation process. Two different kinds of deformation based on edges and information obtained from tissue segmentation are used to find different parts of the complex. A new energy is defined to use tissue information. This energy is adopted to expand the model to embed dominant gray matter points in the volume and also withdraw from dominant white matter and CSF points. The initial shape is divided into several parts. In the normal direction of the center of each part, we construct a profile which search for the best point that maximizes this new energy. This algorithm is reliable for finding the overall shape of the complex. It overcomes the poor features of the complex such as weak edges and noise. The algorithm is examined on 5 different subjects and validated using two different validation methods.
Modified Fast Marching Tractography Algorithm and Its Ability to Detect Fibre Crossing

White matter fibre tractography is a non-invasive method for reconstructing three dimensional trajectories of fibre pathways. Fast Marching is one of fibre tracking methods in which co-linearity of principal eigenvectors determines the speed of front's evolution. In this algorithm effect of tensor's eigenvalues are not considered. In the current work, the speed function of standard fast marching was modified by considering the strength of tensor's eigenvectors. The proposed speed function has an adaptive Fractional Anisotropy (FA) weighted factor which can be set by type of brain's environments (i.e. isotropic and anisotropic regions). This modification was found to have high accuracy for detecting fibres by reducing false pathways. The proposed method has performed high accuracy in detection of fibre crossing.
Topology tracking of static and dynamic networks based on structural equation models

Most of the complex networks have hidden topologies, therefore, their structures must first be modeled in order to conduct meaningful network analytics. Structural equation models (SEMs) are from prominent network models and they often express the relationship between exogenous inputs of the network and outputs linearly. In this paper, based on SEMs, we propose a method to track the topology of static and dynamic networks over time. The static networks have fixed topologies while the topology of the dynamic networks changes over time. The proposed tracking algorithm will improve the topology estimation in static networks, and trace the changes of topology in dynamic networks. The important advantage of the proposed method is about exogenous inputs. Ordinary SEMs assume full knowledge of the exogenous inputs, which may not always be a correct hypothesis. We assume that the exogenous inputs are piecewise stationary and in each piece, the correlation matrix of the exogenous inputs is known, which is a more practical assumption than given exogenous inputs. Numerical tests demonstrate the effectiveness of the proposed algorithm in tracking the topology of static and dynamic networks.
Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles

In medical domains with low tolerance for invalid predictions, classification confidence is highly important and traditional performance measures such as overall accuracy cannot provide adequate insight into classifications reliability. In this paper, a confident-prediction rate (CPR) which measures the upper limit of confident predictions has been proposed based on receiver operating characteristic (ROC) curves. It has been shown that heterogeneous ensemble of classifiers improves this measure. This ensemble approach has been applied to lateralization of focal epileptogenicity in temporal lobe epilepsy (TLE) and prediction of surgical outcomes. A goal of this study is to reduce extra operative electrocorticography (eECoG) requirement which is the practice of using electrodes placed directly on the exposed surface of the brain. We have shown that such goal is achievable with application of data mining techniques. Furthermore, all TLE surgical operations do not result in complete relief from seizures and it is not always possible for human experts to identify such unsuccessful cases prior to surgery. This study demonstrates the capability of data mining techniques in prediction of undesirable outcome for a portion of such cases.
Attribute ranking for lateralizing focal epileptogenicity in temporal lobe epilepsy

A consensus feature-ranking approach has been applied to the study of localization-related temporal lobe epilepsy (TLE) in order to evaluate the relative discriminative power of individual attributes. Cases were selected on the basis of a postoperative outcome free of disabling seizures (i.e., Engel class I) in order to establish a definitive laterality of focal epileptogenicity. Several quantitative measures made available by imaging and electrographic studies are considered and the most discriminative of these are quantitatively prioritized for the lateralization of focal epileptogenicity. Cases requiring extraoperative electrocorticography were examined as a subgroup to establish whether the current method of analysis could distinguish laterality sufficiently well to avoid the requirement for intracranial electrode implantation.
Altered connectivity of task related functional networks in healthy aging

Although the cognitive deficits due to age-related brain differences have been largely analyzed, the altered connectivity of task related functional networks in aging requires more studies. As the brain of old adults experience some alterations in task performance during cognitive challenges, the related effects on connectivity of functional networks are here evaluated using event-related functional Magnetic Resonance Imaging (fMRI). The fMRI data have been acquired for simple visual and motor tasks from 18 subjects (8 young and 10 elderly). For each subject, several Functional Connectivity (FC) networks including, motor, visual and the default mode networks are firstly calculated using a conventional voxel-wise correlation analysis with predefined region of interest. Then, the strength of functional connectivity is assessed and compared for different age groups. The current study has evaluated three hypotheses on FC of aging brain: the frontal regions involved with motor network try to compensate for declines in the posterior regions, default-mode network are less suppressed and, the posterior regions involved with visual network exhibit less connectivity. The first two hypotheses are accepted by analysis results but visual network behaves differently. Also, results show that the task related functional connectivity is considerably altered in old adults compared to young adults. Old adults demonstrate higher connectivity strength on average with a slightly smaller variance than young adults.
AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY CHARACTERISTICS

Water diffusion measurements have been shown to be sensitive to tissue cellular size, extra cellular volume, and membrane permeability. Therefore, diffusion tensor imaging (DTI) by MRI can be used to characterize highly cellular regions of tumors versus acellular regions, distinguishing cystic regions from solid regions. An automatic segmentation method is proposed in this paper based on a multi-phase clustering algorithm to segment the brain tumors in a feature space extracted from DTI images. The algorithm is applied on images of a total of 20 patients with 4 different types of tumors. The tumor region segmentation was 92% accurate based on the segmentation results using anatomical images and 100% accurate based on biopsy results. In general, the segmentation results obtained by the proposed method revealed a strong agreement with the biopsy results and anatomical images, providing support for the accuracy and robustness of the proposed feature space and the segmentation procedure.
Gait Recognition Using Wavelet Packet Silhouette Representation and Transductive Support Vector Machines

Gait is an idiosyncratic biometric that can be used for human identification at a distance and as a result gained growing interest in intelligent visual surveillance. In this pa- per, an efficient gait recognition method based on describing subject outer body contour deformations using wavelet packets is proposed. With the use of Matching Pursuit algorithm, k bases of wavelet packet tree that have maximum similarity to the signal are selected and corresponding coefficients are used as features. Finally, Transductive Support vector Machine (TSVM) classification is utilized on computed eigengait space for semi-supervised identification. The proposed method of selecting features which uses a complete orthogonal or near orthogonal basis from a wavelet packet library of bases and investigating the correlational structure of gait features for each individual using TSVM, result in encouraging identification performance.
Evaluation of BOLD Sensitivity Using a Realistic MRI Simulator

MRl simulation is a suitable method to evaluate and acquire understanding of the complex blood oxygenation level dependent (BOLD) effect. To study the observability of BOLD signal, we extended a MRI simulator. This simulator is capable of complete modeling of object (tissue) and main field inhomogeneities. We made a digital phantom in which the magnitude of fractional oxygenation (Y), hematocrit (Hct) values in red blood cells, the diameter of vessels, and also the position of blood vessel within the voxel can be changed. Then we created echo planar imaging (EPI) images of the phantom under resting and activated states using the simulator. Resulting images were analyzed using cross-correlation and t-test analysis for activation detection. Then the effect of various parameters on the detection of BOLD is investigated. These parameters include fractional oxygenation, spatial extent of BOLD effect and the location of vessel inside voxel. This simulation study provides a basis for planning experiments aimed at BOLD contrast detection with EPI pulse sequence.
Support Vector Machine with nonlinear-kernel optimization for lateralization of epileptogenic hippocampus in MR images

Surgical treatment is suggested for seizure control in medically intractable epilepsy patients. Detailed pre-surgical evaluation and lateralization using Magnetic Resonance Images (MRI) is expected to result in a successful surgical outcome. In this study, an optimized pattern recognition approach is proposed for lateralization of mesial Temporal Lobe Epilepsy (mTLE) patients using asymmetry of imaging indices of hippocampus. T1-weighted and Fluid-Attenuated Inversion Recovery (FLAIR) images of 76 symptomatic mTLE patients are considered. First, hippocampus is segmented using automatic and manual segmentation methods; then, volumetric and intensity features are extracted from the MR images. A nonlinear Support Vector Machine (SVM) with optimized Gaussian Radial Basis Function (GRBF) kernel is used to classify the imaging features. Using leave-one-out cross validation, this method results in a correct lateralization rate of 82%, a probability of detection for the left side of 0.90 (with false alarm probability of 0.04) and a probability of detection for the right side of 0.69 (with zero false alarm probability). The lateralization results are compared to linear SVM, multi-layer perceptron Artificial Neural Network (ANN), and volumetry and FLAIR asymmetry analysis. This lateralization method is suggested for pre-surgical evaluation using MRI before surgical treatment in mTLE patients. It achieves a more correct lateralization rate and fewer false positives.
Automatic dental CT image segmentation using mean shift algorithm

Identifying the structure and arrangement of the teeth is one of the dentists' requirements for performing various procedures such as diagnosing abnormalities, dental implant and orthodontic planning. In this regard, robust segmentation of dental Computerized Tomography (CT) images is required. However, dental CT images present some major challenges for the segmentation that make it difficult process. In this research, we propose a multi-step approach for automatic segmentation of the teeth in dental CT images. The main steps of this method are presented as follows: 1-Primary segmentation to classify bony tissues from nonbony tissues. 2-Separating the general region of the teeth structure from the other bony structures and arc curve fitting in the region. 3-Individual tooth region detection. 4-Final segmentation using mean shift algorithm by defining a new feature space. The proposed algorithm has been applied to several Cone Beam Computed Tomography (CBCT) data sets and quality assessment metrics are used to evaluate the performance of the algorithm. The evaluation indicates that the accuracy of proposed method is more than 97 percent. Moreover, we compared the proposed method with thresholding, watershed, level set and active contour methods and our method shows an improvement in compare with other techniques.
A new scheme for evaluation of air-trapping in CT images

Air trapping is an abnormal retention of air that occurs after expiration state in the lungs, observed in all types of bronchiolar and obstructive lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and bronchiolitis obliterans syndrome. Air trapping is often incidentally diagnosed on computed tomography (CT) scanning but this method needs doctors so it is subjective and depends on their experience. In this paper, we present a novel method for evaluation of air trapping in the lungs for detection of COPD in CT images. The proposed method finds volumetric variations of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared and the volumetric variations are used to classify the subjects. In the evaluated cases, the proposed method is able to estimate air trapping in the lungs from CT images without human intervention. This method may assist radiologists to measure and evaluate air trapping for detection of COPD as a computer aided diagnosis (CAD) system.
Directed epileptic network from scalp and intracranial EEG of epileptic patients

We proposed recently the computation of epileptic connectivity graphs based on wavelet correlation coefficients between EEG signals. The suspected epileptiform electrodes are recognized using the clustering of the topological properties of the graph that can be useful for pre-surgical studies. Here, we present a method for comparing epileptic networks estimated from scalp and intracranial EEG (IEEG) in partial epilepsy patients. The results are presented for a patient with left temporal epilepsy. Good spatial correspondence between the IEEG and the scalp EEG epileptic graphs is obtained. These results are consistent with the patient's clinical diagnosis.
Effects of improved Adaptive Gamma Correction Method on Wireless Capsule Endoscopy images: Illumination compensation and edge detection

Several methods for adjusting image contrast have been developed in the field of image processing for image enhancement. Illumination is one of the most significant factors affecting the appearance of an image. Improper illumination causes a variety of problems in practice and thus, it is necessary to compensate for nonuniform illumination. Wireless Capsule Endoscopy (WCE) images suffer from this problem too. We propose an improved Adaptive Gamma Correction Method (AGCM) to compensate for nonuniform illumination in WCE color images.The experiments have shown that the proposed AGCM performs well in improving image quality. The WCE images obtained with AGCM are also better than those obtained with histogram equalization and adaptive histogram equalization in terms of brightness constancy, overall visual rendition and contrast. The Peak Signal to Noise Ratio (PSNR) and Contrast to Noise Ratio (CNR) values are also higher for AGCM than for other correction algorithms. Also, the differences between edge pixels and neighboring pixels in the WCE images are increased. We use a color variant of the canny operator for color edge detection. By using this method, 65% of the borders are detected correctly, while the detection rate is 45% without using this method.
Determination of pitch range based on onset and offset analysis in modulation frequency domain

Auditory scene in a natural environment contains multiple sources. Auditory scene analysis (ASA) is the process in which the auditory system segregates a scene into streams corresponding to different sources. The determination of range of pitch frequency is necessary for segmentation. We propose a system to determine the range of pitch frequency by analyzing onsets and offsets in modulation frequency domain. In the proposed system, first the modulation spectrum of speech is calculated and then, in each subband onsets and offsets will be detected. Thereafter, the segments are generated by matching corresponding onset and offset front. Finally, by choosing the desired segments, the rage of pitch frequency is determined. Systematic evaluation shows that the range of pitch frequency is estimated with good accuracy.