nils-herrmann/clean_scirepeval huggingface dataset:
Development of a variational Bayesian expectation maximization (VBEM) method for model inversion of multi-area E/MEG model

We develop and evaluate a variational Bayesian expectation maximization (VBEM) method for model inversion of our multi-area extended neural mass model (MEN) using EEG/MEG data. Parameters of MEN have suitable prior distributions that enable us to use properties of a conjugate-exponential model in implementing VBEM. Consequently, VBEM leads to analytically tractable forms that starts with initialization and consists of repeated iterations of a variational Bayesian expectation step (VB E-step) and a variational Bayesian maximization step (VB M-step). Posterior distributions of model parameters are updated in the VB M-step. Distribution of the hidden state is updated in the VB E-step using variational extended Kalman smoother. We evaluate and validate performance of VBEM method for model inversion of MEN using simulation studies in various signal-to-noise ratios. The proposed approach provides a useful technique for analyzing effective connectivity using non-invasive EEG and MEG methods.
Radon transform orientation estimation for rotation invariant texture analysis

This paper presents a new approach to rotation invariant texture classification. The proposed approach benefits from the fact that most of the texture patterns either have directionality (anisotropic textures) or are not with a specific direction (isotropic textures). The wavelet energy features of the directional textures change significantly when the image is rotated. However, for the isotropic images, the wavelet features are not sensitive to rotation. Therefore, for the directional textures, it is essential to calculate the wavelet features along a specific direction. In the proposed approach, the Radon transform is first employed to detect the principal direction of the texture. Then, the texture is rotated to place its principal direction at 0 degrees. A wavelet transform is applied to the rotated image to extract texture features. This approach provides a features space with small intraclass variability and, therefore, good separation between different classes. The performance of the method is evaluated using three texture sets. Experimental results show the superiority of the proposed approach compared with some existing methods.
Segmentation of major temporal arcade in angiography images of retina using generalized hough transform and graph analysis

Structural analysis of the vascular architecture of the retina facilitates the diagnosis of retinopathy because retinopathy affects the blood flow velocity. To this end, blood flow velocity in the major temporal arcade (MTA) vessel in retina may be measured to detect blood flow velocity changes. For this measurement, separation of the MTA vessel is a necessary step. We propose methods for identifying the MTA vessel. We use Gabor filters to detect retinal vessels and Hough transform to model the MTA vessel. Then, we use the MTA model to find a seed point for image segmentation. Next, we take advantage of graph theory to track temporal arcade vessels and separate the MTA vessel. We have evaluated our algorithm using 43 clinical angiography images, in which traces of MTA was drawn by an ophthalmologist. Experimental results show that the proposed methods correctly separate MTA in 93%.
Segmentation of nucleus and cytoplasm of white blood cells using Gram-Schmidt orthogonalization and deformable models

Automatic recognition of white blood cells in hematological can be divided into four major parts: preprocessing, image segmentation, feature extraction and classification. Due to the multifarious nature of these cells and uncertainty in the hematological images, segmentation of white blood cells is one of the most important stages in this process. A scrupulous segmentation obviously reduces errors of next stages. In this paper, we introduce a novel method based on Gram-Schmidt process and parametric deformable models for segmenting the nucleus and cytoplasm. Also, we propose a new preprocessing method for improving the results of cytoplasm segmentation. Moreover, for finding the initial contour for parametric deformable model, an automatic scheme is defined. Experimental results show that our proposed method is capable of segmenting the white blood cells in the hematological images. To evaluate the proposed algorithm quantitatively, we compare its results with the manual segmentations by a hematologist. This study shows robustness of the proposed method. Another feature of the proposed method is that it is simple to implement.
Spectral clustering of resting state fMRI reveals default mode network with specifically reduced network homogeneity in major depression

Although resting state fMRI seems an ideal tool for investigating clinical populations, especially in case of reduced cooperation or tolerance, unbiased methods with high sensitivity for disease relevant pathologies remain to be identified. In this paper, we perform spectral clustering on the mean time series of automated anatomical labeling regions of interest for comparing the resting state networks in healthy volunteers and major depression disorder (MDD) patients. A new network homogeneity measure is suggested as a criterion for evaluating the level of homogeneity in a network. We found reduced network homogeneity specifically within the default mode network in MDD subjects compared to age-matched controls. In contrast to previously proposed methods investigating network homogeneity, we fully relied on data-driven definition of clusters of interest to fill an important gap between ROI based network analyses and those using ICA.
Optimal linear transformation for MRI feature extraction

Presents development and application of a feature extraction method for magnetic resonance imaging (MRI), without explicit calculation of tissue parameters. A three-dimensional (3-D) feature space representation of the data is generated in which normal tissues are clustered around pre-specified target positions and abnormalities are clustered elsewhere. This is accomplished by a linear minimum mean square error transformation of categorical data to target positions. From the 3-D histogram (cluster plot) of the transformed data, clusters are identified and regions of interest (ROIs) for normal and abnormal tissues are defined. These ROIs are used to estimate signature (feature) vectors for each tissue type which in turn are used to segment the MRI scene. The proposed feature space is compared to those generated by tissue-parameter-weighted images, principal component images, and angle images, demonstrating its superiority for feature extraction. The method and its performance are illustrated using MRI images of an egg phantom and a human brain.
Finding the Number of Clusters in a Dataset Using an Information Theoretic Hierarchical Algorithm

One of the most challenging problems of clustering is detecting the exact number of clusters in a dataset. Most of the previous methods, presented to solve this problem, estimate the number of clusters with model based algorithms, which are not able to detect all types of clusters and also face a problem in detecting coupled clusters in a dataset. In this paper we propose a new method for finding the number of clusters in a dataset utilizing information theory and a top-down hierarchical clustering algorithm. The algorithm starts from a large number of clusters and reduces one cluster in any iteration and then allocates its data points to the remaining clusters. Finally, by measuring information potential, the exact number of clusters in a desired dataset is detected. Our method shows high capability and stability in detecting the number of clusters even in complex datasets, as it is computational efficient too. We show the effectiveness of the proposed method by experimenting on several artificial and real datasets and comparing its results with two developed methods for finding the number of clusters in a dataset. The comparisons show superiority of the proposed method.
Numerical solutions to the Bloch-McConnell equations with radio frequency irradiation scheme for CEST MRI

Chemical exchange saturation transfer (CEST) is a new mechanism of contrast generation in magnetic resonance imaging (MRI) which differentiates molecule biomarkers via chemical shift. CEST MRI contrast mechanism is complex and depends on radio frequency (RF) pulses used in experimental conditions. To find the optimal RF pulse, numerical solutions of Bloch-McConnell equations may be used. The purpose of this work is to compare the effects of different RF pulses on the CEST phenomenon. First, a two-pool exchange model for CEST in MRI is used. Z-spectra and signal calculations for the two-pool model have been carried out for some RF pulses. Efficiency of both continuous and binomial RF pulses is investigated by calculating three parameters, namely: CEST image contrast, flip angle and specific absorption ratio (SAR). The contrast of CEST image is considered proportional to amount of saturation. As a binomial RF pulse, WALTZ-16* demonstrates a good efficiency, but generates some unwanted direct saturations. On the other hand, continuous RF pulses do not create any unwanted direct saturation, but have less efficiency.
A comparative analysis of several transformations for enhancement and segmentation of magnetic resonance image scene sequences

The performance of the eigenimage filter is compared with those of several other filters as applied to magnetic resonance image (MRI) scene sequences for image enhancement and segmentation. Comparisons are made with principal component analysis, matched, modified-matched, maximum contrast, target point, ratio, log-ratio, and angle image filters. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), segmentation of a desired feature (SDF), and correction for partial volume averaging effects (CPV) are used as performance measures. For comparison, analytical expressions for SNRs and CNRs of filtered images are derived, and CPV by a linear filter is studied. Properties of filters are illustrated through their applications to simulated and acquired MRI sequences of a phantom study and a clinical case; advantages and weaknesses are discussed. The conclusion is that the eigenimage filter is the optimal linear filter that achieves SDF and CPV simultaneously.
Neural network and fuzzy clustering approach for automatic diagnosis of coronary artery disease in nuclear medicine

We investigated feasibility of using fuzzy clustering and artificial neural network to predict coronary artery disease (CAD) in acute phase from planar and gated SPECT nuclear medicine images. We developed an automatic computerized scheme that helps physicians diagnose coronary artery disease based on 99Tc-Sestamibi myocardial perfusion images. Our study consisted of two separate studies with respect to patient population and imaging method. The first study included 58 subjects (30 male, 28 female) studies using planar rest and stress imaging and a second patient subset of 115 subjects (61 male, 54 female) using gated rest/stress SPECT imaging. After the myocardial perfusion scans, patients also had coronary angiography within three months of the imaging. Signal-to-noise ratio was improved by segmentation of myocardium from its background in both studies using fuzzy clustering with the Picard iteration algorithm. We extracted a set of adaptive features consistent with nature of nuclear medicine imaging and myocardium anatomy. Features were optimized and selected based on maximum separation in multidimensional feature space. A back-propagation artificial neural network (ANN) classifier was trained and tested for each study using the optimal features and the results of coronary angiographies as input and outputs, respectively. ANNs were trained, optimized, and tested using leave-one-out and Poh's Implementation of Weigned-Rumelhart-Huberman (PIWRH) methods, to diagnose the normal and abnormal patients based on their coronary angiograms. The performances of the optimal ANNs were analyzed by receiver operator characteristic (ROC) method.  ANN in the first study were compared to those of the physicians in nuclear medicine ward and two other physicians using ROC method. Results of ANN for the second study were compared to those of the nuclear medicine ward using ROC method. Both subsets demonstrate that the proposed method outperforms visual diagnosis and is therefore a useful adjunct for CAD diagnosis from planar and gated SPECT images.
Constrained optimization of nonparametric entropy-based segmentation of brain structures

We propose a constrained, three-dimensional, nonparametric, entropy-based, coupled, multi-shape approach to segment subcortical brain structures from magnetic resonance images (MRI). The proposed method uses PCA to develop shape models that capture structural variability. It integrates geometrical relationship between different structures into the algorithm by coupling them (limiting their independent deformations). On the other hand, to allow variations among coupled structures, it registers each structure separately when building the shape models. It defines an entropy-based energy function, which is minimized using quasi-Newton algorithm. To this end, probability density functions (pdf) are estimated iteratively using nonparametric Parzen window method. In the optimization algorithm, constraints are used to improve segmentation quality. These constraints are extracted from training data. Sample results are given for the segmentation of caudate, hippocampus, and putamen, illustrating highly superior performance of the proposed method compared to the most similar methods in the literature.
Optimal feature space for MRI

We present development and application of an optimal feature space for magnetic resonance imaging (MRI), without explicit calculation of tissue parameters. We generate a three-dimensional (3D) feature space representation of MRI using a linear transformation that optimizes clustering properties of data. The optimal transformation maximizes the ratio of interset to intraset Euclidean distance for normal tissues subject to the constraint that these tissues be clustered at prespecified target positions. In the resulting 3D feature space, abnormalities are clustered at positions different from the prespecified locations for normal tissues. From the 3D histogram (cluster plot), we identify clusters and define regions of interest (ROIs) for normal and abnormal tissues. These ROIs are used to estimate signature (feature) vectors for each tissue type which in turn are used to segment the image. The proposed feature space has been compared to those generated by tissue-parameter-weighted images, principal component images, and angle images, demonstrating its superiority for feature extraction . Here, the method and its performance are illustrated using MR images of phantoms and human brains with tumor.
Multi-scale approach for retinal vessel segmentation using medialness function

Automated segmentation of retinal vessels in optic fundus images has been the most prevailing effort in many researches during recent years. In this paper, we propose a multi-scale method based on a weighted 2D medialness function. The result of the medialness function is first multiplied by the eigenvalues of the Hessian matrix in every pixel of the image in order to extract vessel's medial-lines. Next, by extracting the centerlines of vessels and estimation of radius of vessels, the retinal vessels are segmented. Finally, the performance of our proposed method is evaluated by the DRIVE and STARE databases and compared with those of several recent methods.
Semi-automatic brain morphometry in CT

Presents development and application of a semi-automatic method for estimating volumes of brain tissues from computed tomography (CT) images. The method does not require manual drawing of tissue boundaries. The steps of the new method are: (1) intracranial brain volume is segmented from skull and background using thresholding and morphological operations; (2) noise is suppressed using a nonlinear edge-preserving filter; (3) a histogram of the resulting image is generated and the dominant peak is removed; (4) minima and maxims of the histogram are identified and the brain is segmented into normal tissues, cerebrospinal fluid, and lesions, if present; (5) previous steps are repeated for each slice and volume of each tissue is estimated from the results. The method has shown to be faster and more reproducible than conventional methods. Details and significance of each step of the method are explained. Experimental results using a simulation, a phantom, and selected clinical cases are presented.<<ETX>>
Shape analysis of hippocampus in temporal lobe epilepsy using Signed Poisson Mapping

Complementary role of computer assisted models using machine learning methods in medical imaging has been a center of attention in recent years. Shape analysis of the brain structures can be used to evaluate their abnormalities and deformations, specifically in patients suffering from neurological diseases like epilepsy, Alzheimer, and Parkinson. We propose an automatic diagnosis and lateralization algorithm using Signed Poisson Mapping (SPoM), which has been recently proposed as a new framework for shape analysis of three-dimensional (3D) structures. In contrast to previous studies, we use a three-class classification to show the robustness of our algorithm in differentiating between normal, left temporal lobe epilepsy (LTLE), and right temporal lobe epilepsy (RTLE) subjects. We also use a support vector machine (SVM) classifier with a radial basic function (RBF) kernel for lateralization, i.e., differentiating between RTLE and LTLE patients. The classification accuracy for the three-class classifier is 94% and for the lateralization task is 95% which is superior to those reported in the related literature.
Automatic segmentation of hippocampus from brain MRI using deformable contours

The application of deformable contours for segmentation of the hippocampus has been investigated. Common problems of dynamic contours in this case and cases like this have been discussed and solved. A new method for extracting the discontinuous boundary of a structure with multiple unwanted edges near it has been developed. The method is based on detecting and following the edge by external forces. The final oscillation of the contour has been remedied by using interpolation of external energy and limiting the movements of the contour. Also, the problem of optimizing the weights has been overcome by making them dependent on the amount of forces.
A Framework for Intracranial Saccular Aneurysm Detection and Quantification using Morphological Analysis of Cerebral Angiograms

Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively.
Quantification of the CEST effect by Gaussian mixture modeling of Z-spectrum

Quantitative evaluation of chemical exchange saturation transfer (CEST) is usually done by solving Bloch-McConnell equations (BME). BMEs are not easily extended and applying them to describe the multi-pool data involves a complex process. In this paper, we developed a Gaussian mixture model (GMM) to represent each component involved in the Z-spectrum by a Gaussian distribution. We then tested and evaluated the GMM for the two-pool exchange site and experimental data. The results showed that GMM is able to fit the experimental data and its accuracy is almost similar to that of the BME model. (average percent of Relative Sum Square Error (%RSSE) <;0.6). Accuracy and simplicity were found to be the advantages of the GMM and lack of analytical relationships among the GMM parameters and physical characteristics of the CEST effect turned out to be its main limitations. We quantified contrast agent (CA) concentration (population fraction of CEST pool) and chemical exchange rate applying the GMM to the simulated data of a two-pool exchange site. It was found that the means and variances of the Gaussians can be used for this purpose. In addition, GMM determines the resonance frequency of each pool easily and accurately because these frequencies are equal to the mean values of GMM.
Counterattack detection in broadcast soccer videos using camera motion estimation

This paper presents a new method for counterattack detection using estimated camera motion and evaluates some classification methods to detect this event. To this end, video is partitioned to shots and view type of each shot is recognized first. Then, relative pan of the camera during far-view and medium-view shots is estimated. After weighting of pan value of each frame according to the type of shots, the video is partitioned to motion segments. Then, motion segments are refined to achieve better results. Finally, the features extracted from consecutive motion segments are investigated for counterattack detection. We propose two methods for counterattack detection: (1) rule-based (heuristic rules) and (2) SVM-based. Experiments show that the SVM classifier with linear or RBF kernel results in the best results.
Robust Estimation and Tracking of Pitch Period Using an Efficient Bayesian Filter

In this paper, we introduce an algorithm for estimating and tracking the pitch period of audio signals using Bayesian filters. For this purpose, we propose a general Bayesian model, which is robust to the nonstationary variations of the amplitude and frequency of the input signal. We also employ a state-space model, which uses the delayed versions of the input signal to model the periodicity of nonstationary audio signals. This simple model allows a significant reduction of the required number of particles for the estimation of the pitch period compared to the state-of-the-art particle filtering methods. Moreover, we propose to estimate the logarithm of the period instead of the period itself. We show that the resulting algorithm does not require prior knowledge about the initial state and is robust to the octave error phenomenon, which is a common problem in pitch period estimation methods. Most of the existing methods require that the processing window be longer than the largest existing period of the input signal. In contrast, the proposed method does not impose such a limit. Our method often results in a higher time-domain resolution with no perceptible compromise on the frequency-domain resolution, especially for high-pitched audio signals such as music. Simulation results reveal that the proposed algorithm outperforms the state-of-the-art pitch period detection algorithms at low signal to noise ratios assuming no prior knowledge about the initial conditions.