nils-herrmann/clean_scirepeval huggingface dataset:
Integrated MEG/EEG and fMRI model based on neural masses

We introduce a bottom-up model for integrating electroencephalography (EEG) or magnetoencephalography (MEG) with functional magnetic resonance imaging (fMRI). An extended neural mass model is proposed based on the physiological principles of cortical minicolumns and their connections. The fMRI signal is extracted from the proposed neural mass model by introducing a relationship between the stimulus and the neural activity and using the resultant neural activity as input of the extended Balloon model. The proposed model, validated using simulations, is instrumental in evaluating the upcoming combined methods for simultaneous analysis of MEG/EEG and fMRI
Classification of fNIRS based brain hemodynamic response to mental arithmetic tasks

Specific characteristics of the functional near infrared spectroscopy (fNIRS) of the hemodynamic response may represent the brain cortical activity levels during mental arithmetic tasks. In this paper, we use hemodynamic response signals of the prefrontal cortex, acquired by a 4-channel fNIRS system to identify the difficulty level of an arithmetic task. To this end, twelve temporal features and several classification methods are used. In addition, most discriminating features are identified by principle component analysis (PCA) method. Experimental results show that the highest accuracy rate of 92.2% is achieved by a linear Support Vector Machine (SVM) classifier. They also show that skewness and total area of the signal from the 3 cm channel on the left prefrontal lobe are the most discriminating features.
A fast algorithm for extrapolation of discrete band-limited signals

A fast algorithm for exact extrapolation of a discrete-time periodic band-limited signal from its known values in an interval having the same length as the bandwidth of the signal is presented. The procedure is a simple autoregression on the time-domain values of the signal, and it is much simpler than previous algorithms for discrete-discrete extrapolation, which required computation of large pseudoinverses. The procedure is highly parallelizable, and the computational savings are especially significant for multidimensional signal extrapolation. Three sample numerical examples are shown: (1) noiseless 1-D extrapolation; (2) noiseless 2-D extrapolation; and (3) band-limited noisy 1-D extrapolation.<<ETX>>
Automatic segmentation of brain structures from MRI integrating atlas-based labeling and level set method

The purpose of this study is to segment hippocampus, amygdala and entorhinal cortex in magnetic resonance images (MRI) of temporal lobe epilepsy (TLE) patients. The proposed method consists of two separate parts. First, we use an atlas-based segmentation method to obtain initial segmentation results for desired structures. Using additional preprocessing steps for image registration and gray matter (GM) segmentation is the specification of this stage of the work. Then, all of the GM voxels are labeled using an anatomical atlas. In the next stage, variational level set formulation without re-initialization is applied on the images. We use the boundaries obtained by atlas-based segmentation as the contour for initialization of level set function. Automatic generation of initial contour makes the final segmentation results operator-independent. The proposed approaches are evaluated by comparing automatic and expertpsilas segmentation results and confirming their similarity.
A feature-based fusion method for making group inference in epileptic fMRI and DTI using canonical correlation analysis

In recent years, there have been a great interest for combined analysis of functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) data, because they present complementary information of different tissue types. Canonical correlation analysis (CCA) is a simple data fusion scheme to evaluate brain connectivity. We specify the versatility of CCA to extract features of resting state fMRI and Diffusion Tensor Imaging (DTI). The most informative features, ALFF and FA, are extracted from datasets of epilepsy and healthy subjects. CCA has been successfully utilized for joint data analysis such as combined analysis of EEG and fMRI of a single subject. In the current work, we present a new technique for combination of two modalities across subjects and back reconstruction of components for each group and each subject. Our results indicate that temporal gyrus, cuneus, posterior cingulate cortex and cingulate gyrus are highly correlated with white matter integrity between two hemispheres (corpus callosum) and cerebro-spinal fluid. In addition, there are significant changes in the thalamus that shows extensive damages in this brain structure.
Uncertain decision-making schemes for knowledge based anatomical landmark localization (K-BALL)

We have proposed a method for localization of anatomical landmarks comprised of two steps: 1) information extraction, and 2) information analysis. The focus of this paper is on the second step during which a set of points, found in the first step, is evaluated. This step utilizes a set of rules and eventually generates a confidence factor (CNF) for each set of points that indicates as to what degree they are good representatives of the "landmarks of interest". We also propose two alternative decision-making schemes: 1) Bayesian networks, and 2) possibilistic inference methods. The latter design is expected to outperform the other two as it utilizes both desired and undesired information. Our simulation study resulted in very similar performance for the rule-based and Bayesian schemes. The overall success rate (average of sensitivity and specificity) of the entire proposed method in localization of the hippocampus on MRI images was 83.3% with an accuracy of 99.2% (using rule-based decision-making scheme).
A multistructural imaging marker for non-invasive lateralization of temporal lobe epilepsy

This study investigates the predictive power of feature sets extracted from different brain structures for lateralization of the epileptogenic focus in mesial temporal lobe epilepsy (mTLE) patients based on imaging features. To this end, volumes of multiple brain structures are extracted from preoperative images of 68 unilateral mTLE patients. Our data set consists of 54 patients with visually observable (positive) hippocampus sclerosis (HS-P) and 14 patients without visible (negative) hippocampus sclerosis (HS-N). Exploiting different structural volumes, contributions of the structures and their correlations to each other is evaluated by data mining techniques. After removing redundant correlated structures, a minimum set of structures is obtained as a mTLE lateralization marker. Our experiments, using volumes of hippocampus, amygdala, and thalamus show a correct lateralization rate of 98.5%. This ternary-structural marker also shows 100% and 93% mTLE lateralization accuracy respectively for HS-P and HS-N groups. In conclusion, the proposed marker improves decision-making of surgical resection especially for HS-N group and may reduce the need for implantation of intracranial monitoring electrodes.
A Novel open-ended intravascular MRI loop probe

Recently, intravascular catheter probes have been developed to increase SNR for MR imaging of coronary arteries. Miniaturization of these catheter probes without degrading their performances is very essential in imaging small arteries. Since both signal and noise received by intravascular loop probes are of low level, the noise generated by the cable connecting the probe to the matching circuit may reduce SNR significantly. Therefore, the tuning and matching circuit must be placed very close to the loop probe, which restricts its miniaturization and flexibility. We propose a novel open-ended loop probe for 64 MHz with an input impedance of 20 Ohm and a length of only 4 cm in the bare case. This has two advantages. Firstly, the matching and tuning circuits of the proposed probe can be located outside the vessel. Secondly, its signal level and uniformity is superior to that of the conventional loop antennas.
Fuzzy Edge Preserving Smoothing Filter Using Robust Region Growing

Smoothing, while preserving edges, has always been a major challenge in image processing. In this paper, we propose a new approach that uses segmentation in order to avoid inter-region smoothing thus preserving the edges. It is common to smooth the image prior to region growing. The opposite procedure does not work properly in the presence of noise since region growing is very noise sensitive. To overcome this difficulty we adapted a robust region growing algorithm. Since region growing is very resource consuming, we do not perform it for every pixel. Instead, we divide the image into a number of overlapping blocks for which we carry out the segmentation. Then, we use the results for some of the core pixels in that block. Now that the regions are known, we proceed to smooth each region by a fuzzy approach emphasizing the pixels residing in it. The performance of this filter is compared to that of the bilateral filter and it is shown that the new method generates superior results.
Comparison of canonical correlation analysis and ICA techniques for fMRI

This paper compares independent component analysis (ICA) and canonical correlation analysis (CCA) applied to functional magnetic resonance imaging (fMRI) data. There has been no systematic comparison of these techniques so far. Two variants of the ICA, Infomax and FastICA, are implemented. The CCA method is investigated according to the signal subspace spanned by two hemodynamic response models: differential Gamma and Balloon models. The criterion for the comparison is the area under receiver operating characteristic (ROC) curve for simulated datasets. This criterion is evaluated for different contrast to noise ratios (CNR). Using a real auditory dataset, the paper also compares the aforementioned algorithms in terms of task-related activation maps. The results indicate the superiority of the CCA for CNRs below 0.75; but as the CNR goes beyond this limit, the ICA with Infomax algorithm outperforms other methods. Furthermore, the use of either differential Gamma or Balloon models in the CCA provides nearly the same performance. The paper results can assist the selection of an appropriate algorithm for fMRI data analysis.
Binaural speech separation based on the time-frequency binary mask

The perceptual ability of the human auditory system in capturing the target voice and filtering out the interferers has been remained as a great challenge. This paper proposes a binaural system for speech segregation based on spatial localization cues: Interaural Time Differences (ITD) and Interaural Intensity Differences (IID). A target speech signal is separated from interfering sounds by estimating time-frequency masks using the multi-level extension of the Otsu thresholding algorithm used in image segmentation. The ITD and IID are important features for mask estimation in low and high frequencies, respectively. A systematic evaluation in terms of Perceptual Evaluation of Speech Quality (PESQ) index shows that the resulting system yields significant improvement in performance of speech separation.
Statistical validation of automatic methods for hippocampus segmentation in MR images of epileptic patients

Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the benchmark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<;0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively.
A framework for fully configurable video coding

Video coding solutions based on fixed, pre-defined video coding standards have certain limitations. Dynamic reconfiguration has the potential to offer greater ratedistortion performance than fixed codecs, making it possible to rapidly implement new algorithms and efficiently support multiple coding formats on a single platform. In this paper we describe a framework for dynamic re-configuration of video codecs, Fully Configurable Video Coding, in which new or modified decoder designs are generated, transmitted and implemented by a Universal Video Decoder. We present initial results of a real-time software prototype.
A Full Reference Quality Metric for Compressed Video Based on Mean Squared Error and Video Content

Visual quality of compressed video sequences depends on factors including spatial texture content and cognition-based factors such as prior knowledge and task in hand. The MOSp metric is a full reference objective quality metric which predicts perceived quality of sequences with video compression-induced impairments based on the spatial texture content and the mean squared error between original and compressed video sequences. In this paper, we extend the MOSp metric to incorporate cognition-based factors to identify regions in a video scene that attract human attention. The proposed metric has been tested on a variety of multimedia sequences of common intermediate format resolution compressed at a wide range of bitrates using the H.264/AVC coding standard. This metric shows a higher correlation with mean opinion score (MOS) than popular metrics, such as peak signal-to noise ratio, National Telecommunications and Information Administration/Institute for Telecommunication Sciences video quality metric, PSNRplus, and the Yonsei University metric. Results also show that by extending the MOSp metric to incorporate cognition-based factors such as skin information, its correlation with subjective scores (MOS) can be significantly improved in video content where humans are present. This algorithm is particularly useful for real-time quality estimation of multimedia sequences with block-based video compression-induced impairments because all the parameters of the metric can be calculated automatically with a modest amount of processing overhead.
A Syntax for Defining, Communicating, and Implementing Video Decoder Function and Structure

Fully configurable video coding is a novel approach to video compression in which a video decoder can be dynamically constructed based on a decoder description. This paper presents a new syntax for describing video decoder functionality and structure, namely, the decoder description syntax (DDS). The DDS is a platform-independent syntax that can define all aspects of a video decoder in terms of basic processing instructions. A DDS description of a video decoder may be coded and transmitted, and then executed by a generic processing platform, a “universal video decoder (UVD).” Any new or modified video decoding function may be described, communicated, and instantiated using the DDS, which makes it possible to rapidly implement new coding algorithms, to dynamically adapt the coding algorithm to suit the video data, and to efficiently implement multiple coding formats on one platform. We present examples of video decoding functions implemented in the DDS and show how these may be executed on a UVD. We demonstrate that flexible configuration, re-configuration, and decoding of video can be achieved using a real-time prototype UVD with functions transmitted and instantiated on-the-fly using the DDS.
A DCT based in-focus visual saliency detection algorithm

A novel low-complexity visual saliency detection algorithm for detecting visually salient regions in images based on camera focus is presented. This fast in-focus region detection algorithm detects salient-frequencies present in in-focus areas using the characteristics of Discrete Cosine Transform coefficients. The performance of this algorithm is validated against five state-of-the-art saliency detection algorithms. The results show that this algorithm consistently outperforms other saliency detection algorithms in terms of complexity and prediction accuracy.
A novel perceptual quality metric for video compression

Modern video compression systems make optimum coding decisions based on rate-distortion performance. Typically the distortion is evaluated as a mathematical error measurement, such as mean squared error (MSE), between the original and compressed video sequences. However, simple automatic error measurements such as MSE do not correlate well with perceptual quality, leading to sub-optimal coding decisions. In this paper we present a new subjective video quality metric that predicts subjective quality of compressed video using temporal and spatial masking information and the MSE between the original and compressed video sequences. The results show that this metric can predict perceptual quality with significantly higher correlation compared to popular metrics such as PSNR, VSSIM and PSNRplus. This algorithm is particularly useful for real-time, accurate perceptual video quality estimation in video applications because all the parameters of the metric can be calculated with a minimal amount of processing overhead.
A new perceptual quality metric for compressed video

This paper presents a new video quality metric for automatically estimating the perceptual quality of compressed video sequences. Distortion measures such as the mean squared error (MSE) and the peak signal to noise ratio (PSNR) have been found to poorly correlate with visual quality at lower bit-rates. The proposed quality metric (MOSp) predicts perceptual quality of compressed video using sequence characteristics and the mean squared error (MSE) between the original and compressed video sequences. The metric has been tested on various video sequences compressed using the H.264 video compression standard at different bit-rates. Results show that the proposed metric has better correlation with subjective quality compared to popular metrics such as PSNR, SSIM and PSNRplus. The new metric is simple to compute and hence suitable for incorporation into real-time applications such as the standard video compression codecs inorder to improve the visual quality of compressed video sequences.
Complexity Control of H.264/AVC Based on Mode-Conditional Cost Probability Distributions

A computational complexity control algorithm is proposed for an H.264 encoder running on a processor/power constrained platform. This new computational complexity control algorithm is based on a macroblock mode prediction algorithm that employs a Bayesian framework for accurate early skip decision. Complexity control is achieved by relaxing the Bayesian maximum-likelihood (ML) criterion in order to match the mode decision threshold to a target complexity level. A feedback algorithm is used to maintain the performance of the algorithm with respect to achieving an average target complexity level, reducing frame by frame complexity variance and optimizing rate-distortion performance. Experimental results show that this algorithm can effectively control the encoding computational complexity while maintaining a good rate-distortion performance at a range of target complexity levels.
Investigation of the effectiveness of video quality metrics in video pre-processing

This paper presents an investigation of the effectiveness of current video quality measurement metrics in measuring variations in perceptual video quality of pre-processed video. The results show that full-reference video quality metrics are not effective in detecting variations in perceptual video quality. However, no reference metrics show better performance when compared to full reference metrics, particularly, Naturalness Image Quality Evaluator (NIQE) is notably better at detecting perceptual quality variations.
Decoder Description Syntax for fully configurable video coding

Fully configurable video coding is a novel approach to video compression in which a video decoder can be dynamically constructed based on a decoder description. This paper presents a new syntax for describing video decoder functionality and structure, namely, the Decoder Description Syntax (DDS). The DDS is a platform-independent syntax that can define all aspects of a video decoder in terms of basic processing instructions. We present examples of video decoding functions implemented in the DDS and show that flexible configuration, reconfiguration, and decoding of video can be achieved using a real-time prototype Universal Video Decoder.
Fast, dynamic configuration of transforms for video coding

This paper examines the computational and rate-distortion performance of a reconfigurable video codec. Dynamic configuration of a video codec has the potential for increased flexibility and rate distortion performance, through enabling adaptation of the coding algorithm to suit sequence statistics or operating conditions. This adaptability may be achieved by sending configuration information to the decoder during a communications session as part of the compressed video signal. This paper presents an example of dynamic re-configurability by sending descriptions of new transforms during coding. A decoder is reconfigured on-the- fly in order to incorporate a new transform, previously unknown to the decoder. We show that a reconfigurable video decoder may be implemented without significant computational penalties and we demonstrate the rate-distortion benefits of dynamic re-configuration.
Implementing Fully Configurable Video Coding

A codec that can be re-configured dynamically has the potential to offer greater rate-distortion performance than fixed codecs by adapting video coding algorithms depending on local statistics and varying system resources. In addition, complete, dynamic configuration of a video decoder makes it possible to rapidly implement new algorithms and efficiently support multiple coding formats on a single platform. This paper describes a framework for dynamic re-configuration of video codecs, Fully Configurable Video Coding, in which new or modified decoder designs are generated, transmitted, and then implemented by a Universal Video Decoder. We present rate-distortion results generated by a real-time prototype of this new approach.
Fast H.264/AVC to HEVC transcoding using mode merging and mode mapping

A fast H.264/AVC to HEVC transcoding algorithm using motion vector based mode merging and conditional probability based mode mapping is proposed in this paper. Reusing the incoming H.264 block modes directly does not give the best Rate Distortion performance in HEVC. Motion vector variance is used to predict the possible merge of the H.264 blocks at coding depths 0 and 1 in HEVC. A conditional probability based mathematical model is developed to predict the two most probable modes at coding depths 2 and 3 of the HEVC encoding stage. Results show around 50 percent complexity reduction with negligible rate distortion loss.