The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Neural spike detection is the very first step in the analysis of recorded neural waveforms for brain machine interface applications and for neuroscientific studies. Spike detection accuracy and algorithm robustness is an important consideration in developing detection algorithms. For real neural recording data without respective ground truth, the evaluation of detection performance is a challenge...
It is a standard approach to consider that images encode some information such as face expression or biomarkers in medical images; decoding this information is particularly challenging in the case of medical imaging, because the whole image domain has to be considered a priori to avoid biasing image-based prediction and image interpretation. Feature selection is thus needed, but is often performed...
In this study, the functional magnetic resonance imaging data from Alzheimer's patients and nondemented older adults are investigated using graph theory. Voxel-based graphs were built on the data obtained from an international data center and their clustering coefficients and characteristic path lengths were calculated. No significant differences were found between the two groups in these two parameters...
The trajectory of early brain development is marked by rapid growth presented by volume but also by tissue property changes. Capturing regional characteristics of axonal structuring and myelination via neuroimaging requires analysis of longitudinal image data with multiple modalities. Complementary to earlier studies of volume and cortical folding analysis, this paper focuses on white matter tissue...
Automated methods for image segmentation, image registration, clustering of images and probabilistic atlas construction are of great interest in medical image analysis. In this work, we propose a model where these different aspects are combined in one comprehensive probabilistic framework. The framework is formulated as an EM optimization algorithm. Validation is performed on simulated and real images...
Glioblastomas are very aggressive cerebral tumors which are characterized by strong proliferative activity which is often assessed using the Ki-67 staining method. We propose a novel clustering method that exhibits high performance to detect Ki-67 hot-spots on immunohistochemical slides of glioblastomas.
One particular challenge in the study of the brain as a complex system is the identification of dynamic functional networks underlying observed neural activity. In this study, we focus on inferring the functional connectivity of the brain and the underlying network patterns from electroencephalography (EEG) data. The interactions between the different neuronal populations are quantified through a...
Clustering analysis is a promising data-driven method for the analysis of functional magnetic resonance imaging (fMRI) data. We use affinity propagation clustering (APC), a new clustering algorithm especially for large data sets, to detect brain functional activation from fMRI in multitask experimental paradigm. The real fMRI study reveals that brain functional activation can be effectively detected...
This paper presents our preliminary study EEG brain signals of children with attention deficit hyperactivity disorder (ADHD) in order to support a computer assisted diagnostic system. The EEG signals were recorded from 13 children including normal and children diagnosed with ADHD. We analyzed the signals with multilevel discrete wavelet decompositions in order to extract brain signal power spectrum...
Estimating white matter ber pathways from a diffusion tensor MRI dataset has many important applications in medical research. Even after the definition of white matter ROIs, the precise selection of appropriate fibers of interest for further analysis in population studies is often a time consuming and error prone task. Tractography segmentation methods based on pairwise distances between fibers are...
Detection of brain tumors from MRI is a time consuming and error-prone task. This is due to the diversity in shape, size and appearance of the tumors. In this paper, we propose a clustering algorithm based on Particle Swarm Optimization (PSO). The algorithm finds the centroids of number of clusters, where each cluster groups together brain tumor patterns, obtained from MR Images. The results obtained...
Artificial Neural Network (ANN) simulates the structure and function of human brain. It has the abilities of parallel information processing, distributed storage and self-learning and reasoning. ANN features fault tolerance, nonlinearity, nonlocality, nonconvexity, etc., and is suitable for identifying and mapping fuzzy information or complex nonlinear relationship. Combined with the characteristics...
This paper presents an automated computed tomography brain segmentation approach used to segment intracranial into brain matters and cerebrospinal fluid in order to detect any asymmetry present. Intracranial midline is used as reference axial where left and right segmented regions are subjectively compared. Two-level Otsu multi-thresholding method has been developed and applied to 213 abnormal cases...
Brain activity can be measured by EEG (Electroencephalogram). The purpose of this research is analysis the dominant location of brain activity in frontal lobe. Frontal lobe is front part of brain that controls the function of planning, problem solving, personality, behaviour, and emotion. BIOPAC MP 30 is used to measure EEG signals during do 6 activities as like listening to Al-Qur'an, reading Al-Qur'an,...
Investigation on novel methods for extracting objects of interest in medical images has been an important and challenging area of research in image analysis. In particular, medical images are highly spatially correlated and subject to fuzzy distribution of pixels, we present in this paper a new algorithm for medical image segmentation with special reference to abdominal aortic aneurysm and degraded...
This paper introduces a new formula for the objective function of the famous fuzzy C-means algorithm. Two weighted terms are added to the objective function to reflect any available information about the class center and class pixels distribution throughout the datasets. The algorithm is evaluated for the task of the segmentation of medical MRI brain volume. The results show that the algorithm has...
Over past few years, the studies of cultured neuronal networks have opened up avenues for understanding the ion channels, receptor molecules, and synaptic plasticity that may form the basis of learning and memory. The hippocampal neurons from rats are dissociated and cultured on a surface containing a grid of 64 electrodes. The signals from these 64 electrodes are acquired using a fast data acquisition...
This paper presents a modified FCM algorithm for segmentation of MRI. The proposed method has introduced by modifying the objective function of the standard FCM and it has the advantage that it can be applied at an early stage in an automated data analysis. The proposed method can deal with the intensity in-homogeneities and image noise effectively. have compared our results with other reported methods...
This paper presents a novel nonparametric clustering algorithm, called energy based evolving mean shift (EMS) clustering. It defines an energy function to characterize the compactness of the underlying data set and proves the clustering procedure converges. Through iterations, the data points collapse into well formed clusters and the associated energy approaches zero. Although as a general algorithm,...
In this study, a novel application of Principal Component Analysis (PCA) is proposed to detect language activation map patterns. These activation patterns were obtained by processing functional Magnetic Resonance Imaging (fMRI) studies on both control and localization related epilepsy (LRE) patients as they performed an auditory word definition task. Most group statistical analyses of fMRI datasets...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.