nils-herrmann/clean_scirepeval huggingface dataset:
Lateralization of temporal lobe epilepsy by imaging-based response-driven multinomial multivariate models

We have developed response-driven multinomial models, based on multivariate imaging features, to lateralize the epileptogenicity in temporal lobe epilepsy (TLE) patients. To this end, volumetrics and statistical quantities of FLAIR intensity and normalized ictal-interictal SPECT intensity on left and right hippocampi were extracted from preoperative images of forty-five retrospective TLE patients with surgical outcome of Engel class l. Using multinomial logistic function regression, the parameters of various univariate and multivariate models were estimated. Among univariate response models, the response model with SPECT attributes and response model with mean FLAIR attributes achieved the lowest fit deviance (65.1±0.2 and 65.5±0.3, respectively). They resulted in the highest probability of detection (0.82) and lowest probability of false alarm (0.02) for the epileptogenic side. The multivariate response model with incorporating all volumetrics, mean and standard deviation FLAIR, and SPECT attributes achieved a significantly lower fit deviance than other response models (11.9±0.1, p <; 0.001). It reached probability of detection of 1 with no false alarms. We were able to correctly lateralize the fifteen TLE patients who had undergone phase II intracranial monitoring. Therefore, the phase II intracranial monitoring might have been avoided for this set of patients. Based on this lateralization response model, the side of epileptogenicity was also detected for all thirty patients who had preceded to resection with only phase I of EEG monitoring. In conclusion, the proposed multinomial multivariate response-driven model for lateralization of epileptogenicity in TLE patients can help in decision-making prior to surgical resection and may reduce the need for implantation of intracranial monitoring electrodes.
Evaluating Effects of Imaging Parameters on Single Cell Detection in Molecular MRI via Simulation

To evaluate the sensitivity and specificity of MR acquisition methods for molecular imaging, we extended a magnetic resonance imaging (MRI) simulator. This simulator is capable of complete modeling of object (tissue) in microscopic level to study the effect of spatial distribution and concentration of nanoparticles. Using this tool we studied gradient-echo (GE), and True-FISP pulse sequences. In terms of detection sensitivity, GE and True-FISP detected individual labeled cells in 9.4 T MRI. We then conducted a systematic study in order to determine the optimal parameters for GE and True-FISP pulse sequences (echo time, repetition time, iron mass per cell, and image resolution). This simulation study provides a basis for planning experiments aimed at single cell detection with GE and True-FISP sequences.
Fusing Iris, Palmprint and Fingerprint in a Multi-biometric Recognition System

This paper presents a trimodal biometric recognition system based on iris, palmprint and fingerprint. Wavelet transform and Gabor filter are used to extract features in different scales and orientations from iris and palmprint. Minutiae extraction and alignment is used for fingerprint matching. Six different fusion algorithms including score-based, rank-based and decision-based methods are used to combine the results of three modules. We also propose a new rank-based fusion algorithm Maximum Inverse Rank (MIR) which is robust with respect to variations in scores and also bad ranking from a module. CASIA datasets for iris, palmprint and fingerprint are used in this study. The experiments show the effectiveness of our fusion method and our trimodal biometric recognition system in comparison to existing multi-modal recognition systems.
Controlling the false positive detection rate in fuzzy clustering of fMRI data

Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomization-based method to control the false positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. Controlling the false positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this paper, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRF-based feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space.
Adapting Medical Image Processing Tasks to a Scalable Scientific Workflow System

In this paper, we present a web-based medical image processing scientific workflow system called DATAVIEW. This platform is implemented to satisfy the need of physicians to process their data by computer scientists and therefore, overcome the problem of delay for communicating between these two groups and speed up the processing which is very important in emergency conditions. For employing this workflow system, clinicians do not require to install any software tools and can create, save, share, reuse, and run their workflows only using a web browser without knowing the implementation details of the service. For employing the DATAVIEW workflow system for the medical image processing purpose, we extend this workflow system by integrating medical image software tools to this platform which is one of the challenges of this research. Also, in order to provide processing the data in parallel, we integrate high-end computing resource like grids to the system to speed up the processing, which is another challenge for this work. This is very important in medical imaging field because some software tools are very time consuming and in the urgent time, the physicians need to process multiple patients in parallel and get the results fast. As a case study, we choose epilepsy, which is one of the most common brain disorders.
Multiwavelet grading of pathological images of prostate

Histological grading of pathological images is used to determine the level of malignancy of cancerous tissues. This is a very important task in prostate cancer prognosis, since it is used for treatment planning. If infection of cancer is not rejected by noninvasive diagnostic techniques like magnetic resonance imaging, computed tomography scan, and ultrasound, then biopsy specimens of tissue are tested. For prostate, biopsied tissue is stained by hematoxyline and eosine method and viewed by pathologists under a microscope to determine its histological grade. Human grading is very subjective due to interobserver and intraobserver variations and in some cases difficult and time-consuming. Thus, an automatic and repeatable technique is needed for grading. The Gleason grading system is the most common method for histological grading of prostate tissue samples. According to this system, each cancerous specimen is assigned one of five grades. Although some automatic systems have been developed for analysis of pathological images, Gleason grading has not yet been automated; the goal of this research is to automate it. To this end, we calculate energy and entropy features of multiwavelet coefficients of the image. Then, we select most discriminative features by simulated annealing and use a k-nearest neighbor classifier to classify each image to appropriate grade (class). The leaving-one-out technique is used for error rate estimation. We also obtain the results using features extracted by wavelet packets and co-occurrence matrices and compare them with the multiwavelet method. Experimental results show the superiority of the multiwavelet transforms compared with other techniques. For multiwavelets, critically sampled preprocessing outperforms repeated-row preprocessing and has less sensitivity to noise for second level of decomposition. The first level of decomposition is very sensitive to noise and, thus, should not be used for feature extraction. The best multiwavelet method grades prostate pathological images correctly 97% of the time.
Novel double-turn loop probe for intravascular MRI

Key challenges for design of intravascular loop probes are minimization of the cross sectional and length while increasing signal-to-noise ratio (SNR). Although NPO grade captors under 1mm dimensions are now available, the impedance matching network components still constrain tunability. In this study, a new loop probe is proposed for intravascular magnetic resonance imaging (MRI) at 1.5 Tesla. The new design is based on a two-turn loop, separated by a gap, which can be placed over a perfused inflatable balloon structure. The length and impedance of the probe are 1.1 cm and 23.5OHM (bare case), respectively. The SNR of the new probe is greater than the conventional loop probe. To evaluate the performance of the probe, a series of SNR, length, and impedance comparisons with the conventional loop probes are carried out.
Multiresolution automatic segmentation of T1-weighted brain MR images

Automatic segmentation of brain tissues is crucial to many medical imaging applications. We use a multi-resolution analysis and a power transform to extend the well-known Gaussian mixture model expectation maximization based algorithm for segmentation of white matter, gray matter, and cerebrospinal fluid from T1-weighted magnetic resonance images (MRI) of the brain. Experimental results with near 4000 synthetic and real images are included. The results illustrate that the proposed method outperforms six existing methods.
A multidimensional nonlinear edge-preserving filter for magnetic resonance image restoration

The paper presents a multidimensional nonlinear edge-preserving filter for restoration and enhancement of magnetic resonance images (MRI). The filter uses both interframe (parametric or temporal) and intraframe (spatial) information to filter the additive noise from an MRI scene sequence. It combines the approximate maximum likelihood (equivalently, least squares) estimate of the interframe pixels, using MRI signal models, with a trimmed spatial smoothing algorithm, using a Euclidean distance discriminator to preserve partial volume and edge information. (Partial volume information is generated from voxels containing a mixture of different tissues.) Since the filter's structure is parallel, its implementation on a parallel processing computer is straightforward. 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 preprocessing procedure for the eigenimage filter. (The eigenimage filter generates a composite image in which a feature of interest is segmented from the surrounding interfering features.) 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.
Identification of brain regions involved in epilepsy using common spatial pattern

In this paper, we aim to identify the regions involved in epilepsy from intracerebral EEG (iEEG) of patients suffering from focal epilepsy. Identification of regions involved in epilepsy is important for presurgery evaluations. The proposed method is based on common spatial pattern (CSP) using two types of time intervals: 1) periods including inter-ictal epileptiform discharges (IED), and 2) periods excluding IEDs or abnormal physiological signals. The method is applied on the iEEG recordings of one seizure-free patient after resective surgery. The results are compared with seizure onset zones visually inspected by the epileptologist. The congruent IED regions with visually detected seizure onset zones are encouraging results. Moreover, the application of CSP method for the identification of IED regions seems interesting as this method is fast and simple.
Automatic segmentation of brain tumors in magnetic resonance images

Segmentation of tumors in magnetic resonance images (MRI) is an important task but is quite time consuming when performed manually by experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue in different patients, and in many cases, similarity between tumor and normal tissues. This paper presents an automatic method for segmentation of brain tumors in MRI. We use images of patients with glioblastoma multiform tumors. After pre-processing and removal of the regions that do not have useful information (e.g., eyes and scalp), we create a projection image for determining the primary location of the tumor. This image provides an overall view of the tumor. Then, we grow the primary region to segment the entire tumor. This method is automatic and independent of the operator. It segments low contrast tumors without requiring their exacta tissue boundaries. The segmentation results obtained by the proposed approach are compared with those of an expert radiologist showing excellent correlations among them (R2=0.97).
Automatic detection of red blood cells in hematological images using polar transformation and run-length matrix

Many of diseases related to red blood cells can be diagnosed by analyzing the hematological images. The first step is to locate precisely the position of red blood cells. In this paper, a novel method based on polar transformation and run-length matrix is proposed for detecting red blood cells in hematological images. The multilayer perceptron was employed for classifying the feature vectors. This technique detected 3022 of the 3092 red blood cells existed in the images studied. The results illustrate capability of the proposed method for detecting red blood cells in hematological images.
Comparison of voxel-based morphometry (VBM) and tractography of diffusion tensor MRI (DT-MRI) in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is a neurological disease that affects millions of individuals in the world. Majority of TLE patients suffer from refractory seizures. Determining abnormal/damaged regions of the brain that harbor seizure focus is a critical step towards treatment of TLE patients. Diffusion tensor imaging (DTI) is a non-invasive method that we use in this work to determine damaged regions of white matter in the brain. To this end, we apply two methods, voxel based morphometry (VBM) and fiber tracking. We use these methods along with two anisotropy maps (EAR and FA) extracted from the DTI data. We use DTI data of 31 subjects (12 normal subjects and 19 TLE patients) for testing and evaluation studies. The VBM method shows that TLE is associated with reduction of both of FA and EAR values in several regions within the temporal lobes. The tractography method shows abnormal diffusion characteristics in three fiber bundles (fornix, uncinate fasciculus, and cingulum). The results show that the EAR maps are less sensitive to noise than the FA maps and the EAR values change more than the FA values in TLE.
Automatic informative tissue's discriminators in WCE

Wireless capsule endoscopy (WCE) is a new device which investigates the entire gastrointestinal (GI) and especially small bowel. About 55000 frames are recorded in an examination for a capsule which captures two frames per second. Thus, it is essential to find an automatic and intelligent method to help physicians. The WCE videos have lots of uninformative parts (such as extraneous matters, bubbled, and dark part), so preprocessing is necessary to separate these uninformative regions in a frame or reduce frames' numbers. In this paper, we introduce two novel methods to detect automatically uninformative parts. In order to achieve this goal, we use two Mathematical Morphological operations, sigmoid function as a method to segment regions, statistic features, Gabor filters, fisher score test to reduce number of features, neural network and discriminators in color space. Our experimental studies indicates that precision, sensitivity, accuracy, and specificity are respectively 96.13%, 95.30%, 96.35% and 97.00% in the first method, and 90.17%, 95.68%, 93.72%, and 92.71%, respectively in the second method.
Reference-Based Source Separation Method For Identification of Brain Regions Involved in a Reference State From Intracerebral EEG

In this paper, we present a fast method to extract the sources related to interictal epileptiform state. The method is based on general eigenvalue decomposition using two correlation matrices during: 1) periods including interictal epileptiform discharges (IED) as a reference activation model and 2) periods excluding IEDs or abnormal physiological signals as background activity. After extracting the most similar sources to the reference or IED state, IED regions are estimated by using multiobjective optimization. The method is evaluated using both realistic simulated data and actual intracerebral electroencephalography recordings of patients suffering from focal epilepsy. These patients are seizure-free after the resective surgery. Quantitative comparisons of the proposed IED regions with the visually inspected ictal onset zones by the epileptologist and another method of identification of IED regions reveal good performance.
A Bayesian averaged response-driven multinomial model for lateralization of temporal lobe epilepsy

To develop a Bayesian averaged multinomial model for lateralization of epileptogenicity in temporal lobe epilepsy (TLE) patients based upon features extracted from preoperative T1-weighted and FLAIR imaging.  A retrospective cohort of seventy-six TLE patients with surgical outcome of Engel class I was quantitatively analyzed to extract hippocampi volumetrics and FLAIR intensity. Using multinomial logistic regression, single response-driven models were estimated. Based on Bayesian model averaging (BMA), a model was developed and its performance was compared with the single response models.  The Bayesian averaged model achieved a lateralization rate of 84.2% for TLE patients that was higher than any single response model. Out of the thirty-four patients who underwent phase II intracranial monitoring, the epileptogenic side was correctly lateralized in nineteen cases.  The proposed response-driven model can improve the decision-making for surgical resection and may reduce the need for implantation of intracranial monitoring electrodes.
A Fast Algorithm for Extrapolation of Discrete-Time Periodic Band-Limited Signals

We present a fast algorithm for exact extrapolation of a discrete-time and periodic band-limited signal from its values in an interval having the same length as the band-width of the signal. Here band-width is the number of non-zero values in one period of the discrete Fourier transform of the signal. Applications to problems in which the number of given values is unequal to the band-width are also given. The procedure is a simple autoregression on the time-domain values of the signal, and is much simpler than previous algorithms for discrete~tiscrete extrapolation, which required computation of a large pseudo-inverse. The procedure is highly paralle- lizable, and the computational savings are especially significant for multidimensional signal extrapolation. Numerical examples for 1 -D and 2-D extrapolation demonstrate ( 1 ) that the procedure works perfectly in the absence of noise, and (2) that it works well in the presence of band-limited noise. In the presence of wide-band noise the procedure breaks down, due to ill-posedness of the problem; some regularization techniques are proposed.
Optimizing the parameters of continuous RF pulses for CEST MRI by numerical solution to the Bloch-McConnell equations

Chemical exchange saturation transfer (CEST) is a new contrast in magnetic resonance imaging (MRI) that isolates molecule biomarkers through chemical shift. CEST MRI contrast depends on radio frequency (RF) power and RF pulse type. Two approaches to saturate contrast agent (CA) protons have been used: continuous wave CEST (CW-CEST) and pulsed CEST. To find the optimal RF pulse, numerical solution of Bloch-McConnell equations (BME) may be used. The purpose of this work is to compare the effects of different CW-CEST. In this paper, Rectangular, Gaussian and Fermi pulses are investigated as CW RF pulses. To compare the efficiency of pulses their resultant Flip angles (FA) are assumed equal. Efficiency of CW-CEST is investigated by two parameters, CEST ratio and specific absorption ratio (SAR). Our results suggest that it is possible to maximize CEST ratio by optimizing parameters of Rectangular (amplitude about 5.7 μT), Gaussian (Ã about 0.7s) and Fermi (avalue about 0.3s). Results are verified by empirical formulation of CEST ratio.
Multiple-atlas-based automatic sementation of hippocampus for lateralization in temporal lobe epilepsy

We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of the energy function, a two-step optimization strategy is used. In the first step, shape parameters are optimized based on the analytic derivatives of the energy function. In the second step, shapes of the structures are fine-tuned using a level set method. The proposed method is shown to be superior to some popular methods in the literature using a dataset of 64 patients with mesial temporal lobe epilepsy. In addition, the method can be used for lateralization with accuracy close to that of manual segmentation.
Structural connectivity of temporal lobe structures detects temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Detection of TLE from neuroimaging data is a challenging task. In many cases, patients with findings from standard clinical examination do not show any identifiable abnormalities in magnetic resonance imaging (MRI). In this study, we propose a method to detect TLE from structural connectivity of temporal lobe structures, using an integration of diffusion tensor imaging (DTI), graph theory analysis, and machine learning tools. To this end, first, we apply probabilistic tractography to extract white matter fibers and generate weighted structural connectivity graphs of 17 TLE patients and 17 healthy control subjects. Machine learning of graph theoretical measures are then used to classify TLE patients and control subjects. Using leave-one-out cross-validation, we show that graph theory measures used in logistic regression classifiers achieve accuracy in the range of 76% to 91% for the classification of the 34 subjects into the TLE and control groups. The results suggest that machine learning of the graph theoretical metrics derived from DTI-based networks of temporal lobe regions may be used for the diagnosis of TLE patients. Such a method may improve diagnostic assessment, especially in patients without obvious abnormalities in the conventional, anatomical neuroimaging examinations.