nils-herrmann/clean_scirepeval huggingface dataset:
Multiresolution fMRI activation detection using translation invariant wavelet transform and statistical analysis based on resampling

A new method is proposed for activation detection in event-related functional magnetic resonance imaging (fMRI). The method is based on the analysis of selected resolution levels (a subspace) in the translation invariant wavelet transform (TIWT) domain. Using a priori knowledge about the activation signal and trends, we analyze their power in different resolution levels in the TIWT domain and select an optimal set of resolution levels. A randomization-based statistical test is then applied in the wavelet domain for activation detection. This approach suppresses the effects of trends and enhances the detection sensitivity. In addition, since TIWT is insensitive to signal translations, the power analysis is robust with respect to signal shifts. The randomization test alleviates the need for assumptions about fMRI noise. The method has been applied to simulated and experimental fMRI datasets. Comparisons have been made between the results of the proposed method, a similar method in the time domain and the cross-correlation method. The proposed method has shown superior sensitivity compared to the other methods.
Information theoretic evaluation of brain connectivity compared to cross correlation using simulated resting state fMRI data

Cross correlation is the most common measure used in the literature to estimate functional connectivity between brain regions using resting state functional magnetic resonance imaging (rs-fMRI) data. However, nonlinearities in the BOLD signal along with intrinsic delay in the response of the remote brain regions deteriorate the performance of this measure. Unlike cross correlation, information theoretic measures are able to detect these nonlinear relations. In this paper, we propose using quadratic mutual information (QMI) for estimating connectivity matrix and compare it with cross correlation using simulated rs-fMRI data. The results show that the proposed measure outperforms cross correlation and other information theoretic measures in estimating the connectivity matrix.
TLE lateralization using whole brain structural connectivity.

A prerequisite of temporal lobe epilepsy (TLE) surgery is to lateralize the disease. Recent studies have shown the capability of diffusion weighted MRI (DWMRI) in lateralizing TLE patients. This has been achieved by analyzing diffusion parameters of specific white matter tracts or regions known to be involved in the disease; however, other brain regions and connections have not been investigated for TLE lateralization. Whole brain structural connectivity using DWMRI provides a wealth of information regarding the structural connections in the brain. This information can be explored to find the most effective connections for TLE lateralization. In this work, we investigate the connectivity matrices calculated from DWMRI of 10 left and 10 right TLE patients to find the most effective connections for lateralizing the disease. Linear support vector machine (LSVM) classifier and leave-one-out cross validation scheme are used to estimate classification performance of the connectivity feature subsets. A subset of three connections with 100% classification accuracy is found. The corresponding LSVM classifier may be used to lateralize prospective TLE patients.
Sparse MEG source localization on parameterized cortical medial surface

The inverse mapping in MagnetoEncephaloGraphy involves localizing neuronal activity of the gray matter tissue from measured signals around the head. The search space of the inverse mapping is conventionally represented in 3-D Cartesian coordinates including the cortical volume, or alternatively, 2-D manifold describing the cortical tissue. 3-D Cartesian coordinates does not suit sparse regularization of the inverse mapping in multi-resolution source domains. On the other hand, boundary surface of the cortex, represented as triangulated mesh, is used as the search space in some other inverse mapping techniques. However, this surface does not represent depth of the cortex. Moreover, to represent neuronal activity in embedded signal dictionaries, convoluted manifold should be gridded. In this paper we propose to use medial surface as the best surface descriptor of the cortical tissue. Furthermore, manifold gridding is constructed by defining bijective mapping between a flattened parameterized domain and convoluted surface. Signal bases are defined conventionally in the parameterized domain and mapped to the cortical volume by means of the bijective mapping. Medial surface of the cortex tissue, represented as triangulated mesh, is extracted by gradient-based search algorithm. Source localization, for simulated neuronal activity, is performed in real head model extracted from magnetic resonance images. Sparse estimated source of the inverse mapping are compared on volume, boundary surface, and medial surface domains. Simulation results confirm advantages of surface over volume domain. Performance measures of source localization proves the medial surface as the best surface describing the cortex volume.
A simulation to tissue homogeneity model for capillary of brain by statistical method

Analytical methods for solving adiabatic equations with non-uniform permeability and real arterial input function (AIF) are complicated. We consider crossing of contrast agent through the capillary and BBB as intrinsically statistical processes. Therefore, it is simulated by a stochastic method (Monte Carlo method). In our model, capillary is divided into multiple sections as in Patlak model and contrast agent moves from one section to other sections. For capillary input, AIF is made by Monte Carlo method from real data. Without solving an equation, we derive the concentration of contrast agent as a function of time and distance in the capillary for normal (not permeable due to BBB) and abnormal capillary with uniform and non-uniform permeability.
Automatic grading of pathological images of prostate using multiwavelet transform

Histological grading of pathological images is used to determine the level of malignancy of cancerous tissues. This task is done by pathologists. Pathologists are inconsistent in these judgments from day to day and from person to person. So the grades are very subjective and furthermore in some cases this is a difficult and time-consuming task. This paper presents a new method for automatic grading of pathological images of prostate based on the Gleason grading system. According to the Gleason grading system, each cancerous specimen is assigned one of five grades. In our method the decision is based on features extracted from the multiwavelet transform of images. Energy and entropy features are extracted from submatrices obtained in decomposition. Then a k-NN classifier is used to classify each image. We also used features extracted by wavelet packet and second order moments to compare various methods. Experimental results show the superiority of the multiwavelet transform compared to other techniques. For multiwavelets, critically sampled preprocessing outperforms repeated row preprocessing and has less sensitivity to noise. We also found that the first level of decomposition is very sensitive to noise and thus should not be used for feature extraction.
Optimal linear transformation for MRI feature extraction

This paper 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 prespecified 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 (ROI's) for normal and abnormal tissues are defined. These ROI's are used to estimate signature (prototype) 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 and scene segmentation. Its relationship with discriminant analysis is discussed. The method and its performance are illustrated using a computer simulation and MRI images of an egg phantom and a human brain.
Spatially Adaptive Kernels for Adaptive Spatial Filtering of fMRI Data

Making use of neighborhood time series is an effective way of noise reduction in fMRI data. However the conventional averaging methods blur activated areas. In this paper, a filter with adaptive kernel is designed such that its kernel size and direction are defined at each voxel. First, for finding the optimum size of kernel, a linear combination of some isotropic Gaussian filters with different variances is used, and optimum value of variance is specified. Then, the appropriate kernel direction is determined by a linear combination of some anisotropic basic filters with various directions. The weights of these linear combinations can be calculated by using the restricted canonical correlation method. The proposed method is compared with a similar method based on the steerable filters and the results show that the proposed method improves the ROC curve and prohibits false spread of the activation areas.
A New Approach to White Blood Cell Nucleus Segmentation Based on Gram-Schmidt Orthogonalization

The differential counting of white blood cells provides invaluable information to hematologist for diagnosis and treatment of many diseases. Manually counting of white blood cells is a tiresome, time-consuming and susceptible to error procedure. Due to the tedious nature of this process, an automatic system is preferable. In this automatic process, Segmentation of white blood cells is one of the most important stages. The nucleus of white blood cells has the most information about the type of white blood cells, thus an exact segmentation seems to be helpful for other stages of automatic recognition of white blood cells. In this paper, we introduced a novel method based on orthogonality theory and Gram-Schmidt process for segmenting the nuclei of white blood cells. For evaluation of results, we compared our proposed method with a hematologist manual segmentation. Results show robustness of this technique for segmentation of nuclei, while this method is very simple to implement.
Impaired resting state networks in temporal lobe epilepsy: A resting state fMRI study

Temporal lobe epilepsy (TLE) is the most common type of human refractory epilepsy and can cause widespread impairments in brain functionality. Previous studies show functional and structural abnormalities in different regions of TLE patients' brain in comparison with normal subjects. However, limited studies have been done on connectivity abnormalities of resting state networks (RSN) in TLE patients and most of them studied default mode network (DMN) only. In this paper, nine independent spatial maps of the brain were identified by applying independent component analysis (ICA) on functional magnetic resonance images (fMRI) of the subjects. These maps were classified into four resting state networks: visual network, auditory network, default mode network, and attention network (including right and left fronto-parietal networks and core network). Functional connectivity between time courses of the nine components were computed and then compared between TLE patients and healthy subjects. Results show that there are significant between-group differences in functional connectivity among different brain networks including decrease in functional connections of visual cortex with two other networks, DMN and attention network, and decrease in functional connectivity between DMN and left fronto-parietal network. An increase in functional connectivity between DMN and core network and also between right fronto-parietal network and auditory network is observed.
Major temporal arcade separation in angiography images of retina using the Hough transform and connected components

Structural analysis of the vascular architecture of the retina is useful in facilitate the diagnosis of retinopathy disease. Retinopathy can affects the blood flow velocity. Measuring blood flow velocity in major temporal arcade (MTA) vessels in retina is one of the approaches to identify blood flow velocity changes. Hence separation of MTA vessel is a necessary step in blood flow velocity computation. We propose methods for separation of the MTA. We use Gabor filters to detect retinal vessels and Hough transform to model the MTA. Afterwards we use the MTA model to find initial point of MTA. Next we take advantage of connected components to track temporal arcade vessel, and separate the MTA. Results obtained with applying our algorithm to 24 clinical angiography images, in which traces of MTA was drawn with an ophthalmologist expert, indicating 92% correct separation of MTA.
Diffusion kurtosis imaging discriminates patients with white matter lesions from healthy subjects

This work illustrates that DKI reveals white matter lesions and also discriminates healthy subjects from patientswith white matter lesions. To show this capability, we have investigated DKI images of a healthy subject and apatient with white matter lesions. The analysis was performed both between and within subjects. Regions of Interest (ROIs) for lesion and normal white matterin the patient images are selected manually (for within subject study) and also the corresponding ROIs in the healthy subject are defined (for between subject study). The results of comparing the estimated values for apparent diffusion and kurtosis parameters show that both Dapp and Kapp can distinguish normal and abnormal tissues. Kapp (Dapp) of the normal regions is greater (lower) than that ofthe abnormal regions. Another investigation over all voxels in the brain shows an important feature of kurtosis in determining white matter lesions.
Linear filter design for CNR enhancement of MR images with multiple interfering features

Maximizing the minimum absolute contrast-to-noise ratios (CNRs) between a desired feature and multiple interfering features, by linear combination of images in an MRI scene sequence, is attractive for medical image analysis and interpretation. In the Journal of Electronic Imaging, vol. 1, no. 2, p. 171-82, (1992), the authors published a general formulation of the problem and a novel solution utilizing the simple and numerically stable method of Gram-Schmidt orthogonalization. Their approach derives explicit solutions for the general case of multiple interfering features in the scene. Here, the authors review the theoretical basis for the filter and present its applications to MRI scene sequences of the human brain with cerebral infarction, tumor, or multiple sclerosis (MS). For these applications, a 20% to 100% improvement for the smallest absolute CNR is obtained.
Discovering true association between multimodal data sets using structured and sparse canonical correlation analysis: A simulation study

Recently, multi-modal data fusion emerged as a comprehensive approach in the field of neuroimaging. As a multivariate approach, canonical correlation analysis (CCA) is frequently used for data fusion. However, the current CCA-based fusion approaches face many problems in the sense of high-dimensionality, multi-colinearity, unimodal feature selection, asymmetric and reshaping of the imaging data into vectors. In this paper, we propose a structured and sparse CCA (ssCCA) technique to overcome the above problems. To investigate the performance of the proposed approach, we compared three multivariate and multi-modal fusion techniques: standard CCA, regularized sparse CCA, and ssCCA, with respect to their ability to detect multi-modal data associations. The results demonstrate that the ssCCA approach has superior recovery performance in the extraction of the true correlated variables relative to the other two fusion approaches.
Detection of uninformative regions in wireless capsule endoscopy images

Wireless capsule endoscopy (WCE) is able to investigate the entire gastrointestinal tract including the small bowel. To reduce the reviewing time of captured images by gastroenterologists and increasing the accuracy rate for automatic detection of abnormalities, it is beneficial to remove regions which have less or no clinical information of small bowel texture (i.e., uninformative regions). In this research study, a multi-stage method including Chan-Vese, color range ratio, adaptive gamma correction method (AGCM), canny color edge detection operator, and morphological processing is proposed to detect these uninformative regions. The results support the effectiveness of the proposed method. In conclusion, the proposed method is a simple method to implement and performed well in removing the uninformative regions of small bowel images.
A hybrid coherent-incoherent method of modulation filtering for Single Channel Speech Separation

Single Channel Speech Separation has been the objective of extensive research in recent years. In this paper, we propose a hybrid system of coherent and incoherent modulation filtering for separation of the target speech from the interference. In the proposed system, subband envelopes are determined using a coherently detected subband carrier based on the time-dependent spectral Center-Of-Gravity (COG) demodulation and then the interference signal is eliminated by applying the adaptive Affine Projection (AP) filter to the subband envelop. The reference signal for the adaptive AP filter is provided by a parallel incoherent system of modulation filtering. Our evaluations, based on several objective measures, indicate that the proposed system extracts the majority of target speech signal segments with minimal interference, outperforming previous systems in voiced speech separation.
Multi-surface quasi-isometric flattening of the cortex

Mapping convoluted surface of the cortex to a canonical space has been widely used in brain functional imaging. Apart from different geometrical features proposed to be preserved during mapping, geometrical features of the cortex tissue differ when traveling from the white matter to the pial boundaries. We propose, for the first time, to represent cortical volume with a set of partial iso-surfaces. Iso-surfaces are defined as the locus of points at constant distance ratio from the cortex boundaries. Such representation provides a specialized frame of reference within the cortex which advocates physiological evidence that active neurons are mainly oriented locally normal to the cortex surface. Furthermore, multi-surface representation is applied to flatten the whole set of iso-surfaces on a planar environment simultaneously. Multi-surface flattening improves flattening performance in terms of lowering distortion variance on the set of iso-surfaces.
Detection of abnormalities in wireless capsule endoscopy frames using local fuzzy patterns

Wireless capsule endoscopy (WCE) is a relatively new technology that captures images of the entire gastrointestinal tract non-invasively and painlessly. A large number of frames are captured during an examination. The main difficulty for physicians is to review the frames visually. In recent years, some methods have been devised to help physicians. Here, a novel method is devised to distinguish among frames whether the frames contain abnormal region or not. This goal is achieved using local fuzzy patterns (LFP) that is compared with rotation invariant local binary patterns (LBP). In addition to this comparison, four color channels (red, hue, green, and gray scale) are examined to find the best one for using LFP. Experimental results indicate that LFP is a powerful method for texture detection, and can develop the LBP results in the WCE frames.
Prediction of glioblastoma multiforme response to bevacizumab treatment using diffusion and perfusion imaging

Response to Bevacizumab in Glioblastoma Multiforme (GBM) is not the same for all patients who receive this antiangiogenic therapy. In this research, structural magnetic resonance images (MRI), Diffusion Tensor Images (DTI), and Dynamic Susceptibility Contrast (DSC) images are used to predict the response to treatment. Apparent Diffusion Coefficient (ADC) and Fractional Anisotropy (FA) maps are calculated from the DTI data. Relative peak height (rPH) and relative percentage of signal intensity to recovery (rPSR) are extracted using DSC images. Histograms are derived from the CE regions of the maps and statistical features are extracted from the histograms. Predictions are done using a logistic regression (LR) algorithm and the Leave One Out Cross Validation (LOOCV) method is applied to evaluate the quality of the prediction models. Using the DTI data, it is found that the median of FA and ADC are capable of predicting the response to treatment with accuracies of 81.8% and 72.7%, respectively (t-test, p-value=0.029, 0.027). Using the DSC images, it is found that rPH is a predictive feature with 87.5% accuracy (t-test, p-value=0.038). Putting the three features (median of ADC and FA and rPH) together, a fully (100%) accurate prediction is achieved (t-test, p-value=0.014). In conclusion, DTI and DSC images have rich information about cellularity and vascularity of the tumor regions in GBM patients and can be helpful in GBM treatment, especially for antiangiogenic therapy.
Directed Differential Connectivity Graph of Interictal Epileptiform Discharges

In this paper, we study temporal couplings between interictal events of spatially remote regions in order to localize the leading epileptic regions from intracerebral EEG (iEEG). We aim to assess whether quantitative epileptic graph analysis during interictal period may be helpful to predict the seizure onset zone of ictal iEEG. Using wavelet transform, cross-correlation coefficient, and multiple hypothesis test, we propose a differential connectivity graph (DCG) to represent the connections that change significantly between epileptic and nonepileptic states as defined by the interictal events. Post-processings based on mutual information and multiobjective optimization are proposed to localize the leading epileptic regions through DCG. The suggested approach is applied on iEEG recordings of five patients suffering from focal epilepsy. Quantitative comparisons of the proposed epileptic regions within ictal onset zones detected by visual inspection and using electrically stimulated seizures, reveal good performance of the present method.
A deformable model for hippocampus segmentation: improvements and extension to 3D

The application of a deformable model to the segmentation of hippocampus in brain MRI has been investigated. Common problems of the model in this case and similar cases have been discussed and solved. A new method for extracting discontinuous boundaries of an object with multiple unwanted edges has been developed. This method is based on detecting and following the edge by external forces. For improving the contour stability, its movement has been limited. Also, adaptive values for internal force weights have been used. In the next step, the model has been extended to 3D which is a Deformable Surface Model. A geometric structure used for this purpose. This helps in definition of normal vectors and internal forces. Finally, a method for generating the initial volume from individual initial polygons has been developed.