nils-herrmann/clean_scirepeval huggingface dataset:
Graph theoretical metrics and machine learning for diagnosis of Parkinson's disease using rs-fMRI

In this study, we investigated the suitability of graph theoretical analysis for automatic diagnosis of Parkinson's disease. Resting state fMRI data from 18 healthy controls and 19 patients were used in the study. After data preprocessing and identifying 90 regions of interest using the AAL atlas, average time series of each region was obtained. Next, a brain network graph was constructed using the regions as nodes and the Pearson correlation between their average time series as edge weights. A percentage of edges with the highest magnitude were kept and the rest were omitted from the graph using a thresholding method ranging from 10% to 30% with 2% increments. Global graph theoretical metrics for integration (Characteristic path length and Efficiency), segregation (Clustering Coefficient and Transitivity) and small-worldness were extracted for each subject and their between group differences were subjected to statistical analysis. Local metrics, including integration, segregation, centrality (betweenness, z-score, and participation coefficient) and nodal degree, were also extracted for each subject and used as features to train a support vector machine classifier. We have shown a statistically significant increase in characteristic path length as well as a decrease in segregation metrics and efficiency in Parkinson's patients. A floating forward automatic feature selection method was used to select the 5 best features from all extracted metrics to classify patients. Our classifier was able to achieve a diagnosis accuracy of ∼95% when subjected to a leave-one-out cross-validation test. These features belonged to cuneus (right hemisphere), precuneus (left), superior (right) and middle (both) frontal gyri which were all previously reported to undergo alterations in Parkinson's disease. This investigation confirmed that global brain network alterations are associated with Parkinson's patients' symptoms and showed the potency of using graph theoretical metrics and machine learning for diagnosing the disease.
Hippocampus Volume and Texture Analysis for Temporal Lobe Epilepsy

It has been previously shown that intensity and volume features of the hippocampus from MR images of the brain are useful in detecting the abnormality and consequently candidacy of the hippocampus for temporal lobe epilepsy (TLE) surgery. In this paper, we have studied and compared volume and texture features of hippocampus for TLE lateralization. Volume and some features describing the intensity non-uniformities are used. We use manual segmentation of the hippocampi from T1 weighted images and map them to the FLAIR images. Wavelet features as well as mean and variance of the hippocampus intensities of the FLAIR slices are used as texture features. A dataset of 55 patients with 28 right and 27 left abnormal sides was used in the experiments. Experimental results show that the volume and intensity features are useful in determining the abnormal side
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 three-dimensional visualization in the field of magnetic resonance imaging (MRI). The authors discuss the optimal transformation for simultaneous segmentation of a desired feature and correction of partial volume averaging effects while maximizing the signal-to-noise ratio (SNR) of the desired feature. It is proved that correction of partial volume averaging effects requires the removal of the interfering features from the scene. It is also proved that correction of partial volume averaging effects can be achieved merely by a linear transformation. It is shown that the optimal transformation matrix is easily obtained using the Gram-Schmidt orthogonalization procedure which is numerically stable. Applications of the technique to MRI simulation, phantom, and brain images are shown. It is shown that in all cases the desired feature is segmented from the interfering features and partial volume information is visualized in the resulting transformed images.
A multi-dimensional non-linear edge-preserving filter for magnetic resonance image restoration

The authors present a multi-dimensional non-linear edge-preserving filter for restoration and enhancement of magnetic resonance images (MRI). The filter uses both of the inter-frame (parametric or temporal) and intra-frame (spatial) information to filter the additive noise from an MRI scene sequence. It combines the approximate maximum likelihood (equivalently, least squares) estimate of the inter-frame pixels, using MRI signal models, with a trimmed spatial smoothing algorithm, using a Euclidean distance discriminator to preserve partial volume and edge information. The filter's structure is parallel, making its implementation on a parallel processing computer trivial. Details of the filter implementation for a sequence of four multiple spin-echo images is explained, and the effects of filter parameters (neighborhood size and threshold value) on the computation time and performance of the filter is discussed. The filter is applied to MRI simulation and brain studies, serving as a pre-processing procedure for the eigenimage filter. It outperforms conventional pre- and post-processing filters, including spatial smoothing, low-pass filtering with a Gaussian kernel, median filtering, and combined vector median with average filtering.<<ETX>>
Polyp detection in Wireless Capsule Endoscopy images by using region-based active contour model

Wireless Capsule Endoscopy (WCE) is an almost new technology to capture images from the whole of Gastrointestinal (GI) tract, noninvasively. WCE is a very useful technology to detect various abnormalities like blood based abnormalities, ulcers and polyps. We note that polyps are growing tissues occur on the surface of tissue instead of inside an organ. Most polyps are not cancerous but if one becomes larger than a centimeter, it can turn into cancer by great chance. So, one of the most important advantages of WCE can be the early detection of polyps and cancers. In this paper we proposed using region-based Active Contour Method (ACM) and geometric feature for automatic detection of polyps. The results on a set of images show that the proposed method can achieve %90.91 accuracy, %100 sensitivity and %83.33 specificity on our data set.
Automated Segmentation and Classification of High Throughput Yeast Assay Spots

Several technologies for characterizing genes and proteins from humans and other organisms use yeast growth or color development as read outs. The yeast two-hybrid assay, for example, detects protein-protein interactions by measuring the growth of yeast on a specific solid medium, or the ability of the yeast to change color when grown on a medium containing a chromogenic substrate. Current systems for analyzing the results of these types of assays rely on subjective and inefficient scoring of growth or color by human experts. Here, an image analysis system is described for scoring yeast growth and color development in high throughput biological assays. The goal is to locate the spots and score them in color images of two types of plates named ldquoX-Galrdquo and ldquogrowth assayrdquo plates, with uniformly placed spots (cell areas) on each plate (both plates in one image). The scoring system relies on color for the X-Gal spots, and texture properties for the growth assay spots. A maximum likelihood projection-based segmentation is developed to automatically locate spots of yeast on each plate. Then color histogram and wavelet texture features are extracted for scoring using an optimal linear transformation. Finally, an artificial neural network is used to score the X-Gal and growth assay spots using the extracted features. The performance of the system is evaluated using spots of 60 images. After training the networks using training and validation sets, the system was assessed on the test set. The overall accuracies of 95.4% and 88.2% are achieved, respectively, for scoring the X-Gal and growth assay spots.
Optimization of MRI protocols and pulse sequence parameters for eigenimage filtering.

The eigenimage filter generates a composite image in which a desired feature is segmented from interfering features. The signal-to-noise ratio (SNR) of the eigenimage equals its contrast-to-noise ratio (CNR) and is directly proportional to the dissimilarity between the desired and interfering features. Since image gray levels are analytical functions of magnetic resonance imaging (MRI) parameters, it is possible to maximize this dissimilarity by optimizing these parameters. For optimization, the authors consider four MRI pulse sequences: multiple spin-echo (MSE); spin-echo (SE); inversion recovery (IR); and gradient-echo (GE). The authors use the mathematical expressions for MRI signals along with intrinsic tissue parameters to express the objective function (normalized SNR of the eigenimage) in terms of MRI parameters. The objective function along with a set of diagnostic or instrumental constraints define a multidimensional nonlinear constrained optimization problem, which the authors solve by the fixed point approach. The optimization technique is demonstrated through its application to phantom and brain images. The authors show that the optimal pulse sequence parameters for a sequence of four MSE and one IR images almost doubles the smallest normalized SNR of the brain eigenimages, as compared to the conventional brain protocol.
Characterizing absence epileptic seizures from depth cortical measurements

In this paper, we are going to localize the onset regions and investigate the dynamics of absence epileptic seizures using local field potential recording by depth electrode. We assume that there are some hidden states (under Markovian model) during the seizure and each spike of the seizure is generated when one of the states is activated. Each state is considered as the linear superposition of a few epileptic activities (substates) and each epileptic activity is described by two characteristics: 1) the spatial topography and 2) the temporal representation. Experimental results demonstrate the generality of the proposed model in characterizing absence epileptic seizures.
Multiscale Face Detection: A New Approach to Robust Face Detection

This paper presents a novel method for detecting multiple frontal faces in still images using multi-scale processing. The main characteristic of this algorithm is its stability in detecting faces with seldom false detections and a high correct detection rate. The novelty of this work comes from the utilization of multiscale detection and using two classifiers to reduce false detections. The algorithm generally has two stages: in the first stage, a face is detected in a unique scale and in the second stage, only the faces that are located in the neighbor scales are accepted as real faces. Consequently, a still image is first resized and scanned block wise, and then each enhanced block is tested for being face. One dimensional Harr wavelet is used for feature extraction, which gives appropriate discriminating features between the face and nonface classes. Detection results at each scale are accumulated in an internal database, so the ultimate detection is prepared based on the mutual detection information between consequent scales. To parameterize both the Bayesian and the simple proposed classifier, 2,643 faces were congregated from famous face databases and more than 10,000 non-face samples were selected from nature images. Experimental results using images gathered from known databases like MIT-CMU show great ability of the proposed algorithm in detecting faces.
Statistical shape analysis of hippocampus in temporal lobe epilepsy based on Laplace-Beltrami eigenfunction level sets

Shape analysis plays an important role in many medical imaging studies. One of the recent shape analysis methods uses the Laplace Beltrami operator. This article studies the deformation of hippocampus in temporal lobe epilepsy based on Laplace Beltrami operator. The level sets of Laplace Beltrami eigenfunctions are automatically extracted and their circumferences are used as a measure of thickness. Statistical maps of corresponding level sets are computed and visualized using an average atlas. The results suggest significant deformation in both ipsilateral and contralateral hippocampi of epileptic patients. Also it is shown that the pattern of deformation in the seizer side and the other hippocampi are different. Relative to the previous statistical shape analysis studies in epilepsy, the proposed method gives comparable results with the advantage of no need for alignment.
Change-point analysis of single-trial EEG of auditory and visual oddball tasks

The brain rhythms defined by specific frequency-bands of electroencephalography (EEG) signal are generally thought to represent diverse cognitive processes which wax and wane with sub-second resolutions at different parts of the cortex. Furthermore, single-trial analysis of EEG is believed to show more realistic pictures of ongoing and event-related activities of the brain with millisecond resolution. Here, we present a nonparametric multiple change-point detection and estimation method for analysis of single-trial EEG recorded during auditory and visual oddball tasks. In a simple attention task like oddball, the frontal cortex of the brain is responsible for distinguishing and responding appropriately to target and standard events. With sub-second resolution at the frontal cortex, we show that the α-band activity changes according to “inhibition timing” hypothesis and the β-band activity is in line with “maintaining the status quo” hypothesis.
Improved particle swarm optimization and applications to Hidden Markov Model and Ackley function

Particle Swarm Optimization (PSO) is an algorithm based on social intelligence, utilized in many fields of optimization. In applications like speech recognition, due to existence of high dimensional matrices, the speed of standard PSO is very low. In addition, PSO may be trapped in a local optimum. In this paper, we introduce a novel algorithm that is faster and generates superior results than the standard PSO. Also, the probability of being trapped in a local optimum is decreased. To illustrate advantages of the proposed algorithm, we use it to train a Hidden Markov Model (HMM) and find the minimum of the Ackley function.
Classification of Alzheimer's disease and mild cognitive impairment: Machine learning applied to rs-fMRI brain graphs

A growing number of studies use resting state functional magnetic resonance imaging (rs-fMRI) to investigate functional alterations in Alzheimer's disease (AD) and Mild cognitive impairment (MCI). In this study, we evaluate the effectiveness of graph theory and machine learning in the diagnosis of AD and MCI at the subject level. Moreover, we explore the effect of different methods of graph construction on the classification accuracy. To this end, the rs-fMRI data from 32 AD, 76 MCI, and 46 healthy elderly subjects are used along with three different connectivity measures: Pearson correlation, bend correlation, and mutual information. Both weighted and binary graphs are created for each subject at different density ranges. Based on each, various graph measures have been extracted. Finally, sequential floating forward selection and support vector machine are utilized. Our results suggest that bend correlation is more effective in the classification of AD and MCI. Moreover, weighted graphs and lower density ranges increase accuracy. Finally, bend correlation along with the weighted graph and density threshold of 10% generate the highest accuracy (93%). This study concludes that rs-fMRI and graph theory may provide a non-invasive means for the diagnosis of AD and MCI.
Development of multimodal neuroimaging models for lateralization of temporal lobe epilepsy

Response-driven multimodal models were developed to lateralize temporal lobe epilepsy (TLE). Neuroimaging features of 138 retrospective TLE patients with Engel class l surgical outcomes were extracted, including the hippocampal volumes, normalized ictal-interictal SPECT and MR FLAIR intensities, mean diffusivity, and cingulate and forniceal fractional anisotropy (FA). Using logistic function regression, univariate and multivariate models were developed. The model incorporated all multivariate attributes for 138 TLE cases that had at least one imaging attribute imputing the missing attributes with the mean values of the corresponding attributes measured in a control cohort. A probability of detection and false alarm of 0.83 and 0.17, respectively, was attained for all cases. For patients undergoing phase II intracranial monitoring, these metrics were 0.90 and 0.10. The proposed multimodal neuroimaging model for lateralization of TLE is relevant for surgical decision-making and, in some cases, may eliminate the need for invasive recording.
Distributed Behavior-based Multi-agent System for Automatic Segmentation of Brain MR Images

A novel multi-agent image segmentation system for MR Images is proposed. The agents are behavior-based autonomous entities that are situated in the image as their environment. The agents have local knowledge and cooperate in an implicit and simple manner to achieve globally rational team behavior. Incremental design of agents and their simple interactions facilitate incorporation of expert knowledge and properties of the specific application in the agents' mind. The system has been applied to MRI brain scans for segmentation of three pairs of structures. The results are satisfactory in terms of both average performance and robustness. The system is also proved to be robust against initialization of the agents.
Assessing lung volumetric variation to detect and stage COPD

Chronic obstructive pulmonary disease (COPD) is a devastating disease. In this paper, we propose a novel method for scoring of air trapping in the lungs for detection and evaluation of COPD. The proposed method finds volumetric changes of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared. For this comparison, the lungs parenchyma in the inspiration and expiration images are found and changes in the surface and volume of the lungs between inspiration and expiration are used to define quantitative measures (features). Using these features, the subjects are classified into two groups of normal class and COPD patients using a Bayesian classifier. In addition, t-tests are applied to evaluate discrimination powers of the features for this classification. The main advantage of the proposed method is its ability to estimate air trapping in the lungs from CT images without human intervention. The proposed method may assists radiologists in the detection of COPD as a computer aided diagnosis (CAD) system. It may also assist them with the scoring of the disease (severity of air trapping in the lungs).
Automatic segmentation of thalamus from brain MRI integrating fuzzy clustering and dynamic contours

Thalamus is an important neuro-anatomic structure in the brain. In this paper, an automated method is presented to segment thalamus from magnetic resonance images (MRI). The method is based on a discrete dynamic contour model that consists of vertices and edges connecting adjacent vertices. The model starts from an initial contour and deforms by external and internal forces. Internal forces are calculated from local geometry of the model and external forces are estimated from desired image features such as edges. However, thalamus has low contrast and discontinues edges on MRI, making external force estimation a challenge. The problem is solved using a new algorithm based on fuzzy C-means (FCM) unsupervised clustering, Prewitt edge-finding filter, and morphological operators. In addition, manual definition of the initial contour for the model makes the final segmentation operator-dependent. To eliminate this dependency, new methods are developed for generating the initial contour automatically. The proposed approaches are evaluated and validated by comparing automatic and radiologist's segmentation results and illustrating their agreement.
A Hierarchical Clustering Based on Mutual Information Maximization

Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.
Noise and Outlier Filtering in Heterogeneous Medical Data Sources

There is a growing interest in studying the common features from multiple data sources. Fusing information from multiple heterogenous data sources promises to identify complex multivariate relationships among the heterogeneous sources. Such relationships can provide additional connectivity across the sources. A common way to analyze the relationships between a pair of data sources based on their correlation is canonical correlation analysis (CCA). CCA seeks for linear combinations of all variables from each dataset with maximal correlation between the two linear combinations. However, the existence of non-informative data points and features makes it challenging for CCA to identify significant relationships among the examined datasets. In this paper, we propose a novel method, NORA, Noise-Outliers Removal Algorithm, that can be used to filter out the non-informative data points and features before applying the CCA. NORA was applied to preprocess two epilepsy modalities, the MRI and neuropsychology, prior to applying CCA to find the association between them. The results show that the proposed method leads to interpretable results when noise plays a significant role in the acquisition of the data.
Particle Swarm Optimization (PSO) of power allocation in cognitive radio systems with interference constraints

Cognitive radio is used for enhancement of spectrum efficiency. Although many works have been accomplished on the power allocation of cognitive radio, limited efforts have considered evolutionary algorithms. In this paper, we study this problem in the cognitive radio networks where interference constraints are defined for protection of quality of service (QoS) for both primary and secondary users. Utilities defined as functions of the signal-to-interference-plus-noise ratio (SINR) are matched for each secondary user which meets Nash's axioms. In general, the region of utilities that meets the constraints is non-convex. It is possible to make simplifications, generate a convex region, and then use common convex optimization approaches to obtain a solution. However, Particle Swarm Optimization (PSO) does not need such simplifications and thus its results are superior to those of the convex optimization methods. PSO is an evolutionary algorithm based on social intelligence, utilized in many optimization problems. PSO is a global optimizations algorithm that does not require the objective function be differentiable as required in classic optimization methods.