Search results for "Format"
showing 10 items of 24643 documents
Neurobiology
2016
In the adult mammalian brain, neurogenesis occurs in the dentate gyrus (DG) of the hippocampus throughout the life span. Neurogenesis has been extensively characterized in rodents and to a lesser extent in primates. Within the hippocampus, the adult neurogenic niche is specifically located in the subgranular zone of the DG, where neural stem/progenitor cells (NSPCs) reside. It has been confirmed that these stem cells, which possess astrocytic features, give rise to intermediate progenitors, immature neurons, and neurons. The NSPCs have radial glia-like morphology and electrolucid cytoplasm, in contrast to the immature neurons whose cytoplasm is electrodense, containing abundant polyribosome…
EEG-based biometrics: effects of template ageing
2020
This chapter discusses the effects of template ageing in EEG-based biometrics. The chapter also serves as an introduction to general biometrics and its main tasks: Identification and verification. To do so, we investigate different characterisations of EEG signals and examine the difference of performance in subject identification between single session and cross-session identification experiments. In order to do this, EEG signals are characterised with common state-of-the-art features, i.e. Mel Frequency Cepstral Coefficients (MFCC), Autoregression Coefficients, and Power Spectral Density-derived features. The samples were later classified using various classifiers, including Support Vecto…
The Relevance of Flow Cytometry for Biochemical Analysis
2001
Flow cytometry (FCM) allows the simultaneous measurement of multiple fluorescences and light scatter induced by illumination of single cells or microscopic particles in suspension, as they flow rapidly through a sensing area. In some systems, individual cells or particles may be sorted according to the properties exhibited. By using appropriate fluorescent markers, FCM is unique in that multiple structural and functional parameters can be quantified simultaneously on a single-particle basis, whereas up to thousands of biological particles per second may be examined. FCM is increasingly used for basic, clinical, biotechnological, and environmental studies of biochemical relevance. In this cr…
Optical tomography from focus
2007
A model and a method providing a 3D reconstruction of a given translucent object from a series of image acquisitions performed with various focus tunings is proposed. The object is imaged by transmission; refraction, reflection and diffusion effects are neglected. It is modeled as a stack of translucent parallel slices and the acquisition process can be described by a set of linear equations. We propose an efficient inversion technique with O(n) complexity, allowing practical applications with a simple laptop computer in a very reasonable time. Examples of results obtained with a simulated 3D translucent object are presented and discussed.
Segmentation of Positron Emission Tomography Images Using Multi-atlas Anatomical Magnetic Resonance Imaging (MRI)
2021
Positron emission tomography (PET), is a medical imaging technique, it provides information about the body’s cellular function rather than its anatomy. However, due to the functional nature of PET images, locating the anatomical structures in such an image remains a challenging task, indeed, PET images only provide very little anatomical information. Segmentation of PET images, therefore, requires the intervention of a medical expert. The expert proceeds to a manual segmentation of a volume slice by slice, which turns out to be very tedious and costly in terms of time. In this article, we present, evaluate, and make available a multi-atlas approach for automatically segmenting human brain P…
Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening
2019
AbstractRecently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method t…
Information-theoretic assessment of cardiovascular-brain networks during sleep
2015
This study was aimed at detecting the structure of the physiological network underlying the regulation of the cardiovascular and brain systems during normal sleep. To this end, we measured from the polysomnographic recordings of 10 healthy subjects the normalized spectral power of heart rate variability in the high frequency band (HF) and the EEG power in the δ, θ, α, σ, and β bands. Then, the causal statistical dependencies within and between these six time series were assessed in terms of internal information (conditional self entropy, CSE) and information transfer (transfer entropy, TE) computed via a linear method exploiting multiple regression models and a nonlinear method combining ne…
SIFT Texture Description for Understanding Breast Ultrasound Images
2014
Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level and high-level descriptors, applied to differentiate the tissues present in breast US images. For the low-level texture descriptors case, SIFT descriptors are extracted from a regular grid. The high-level texture descriptor is build as a Bag-of-Features (BoF) of SIFT descriptors. Experimental results are provided showing the validity of the proposed approach for describing the tissues in breast US images.
Clustering Algorithms for MRI
1991
Magnetic Resonance Imaging (MRI) plays a relevant role in the design of systems for computer assisted diagnosis. MR-images are multi-dimensional in nature; physicians have to combine several perceptual information images to perform the tissue classification needed for diagnosis. Automatic clustering methods help to discriminate relevant features and to perform a preliminary segmentation of the image; it can guide the final manual classification of body-tissues. Three clustering techniques and their integration in a MRI-system are described. Their performance and accuracy was evaluated on synthetic and real image-data. A comparison of our approach with the tissue-classification done by a rad…
Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition
2018
Tensor decomposition has been widely employed for EEG signal processing in recent years. Constrained and regularized tensor decomposition often attains more meaningful and interpretable results. In this study, we applied sparse nonnegative CANDECOMP/PARAFAC tensor decomposition to ongoing EEG data under naturalistic music stimulus. Interesting temporal, spectral and spatial components highly related with music features were extracted. We explored the ongoing EEG decomposition results and properties in a wide range of sparsity levels, and proposed a paradigm to select reasonable sparsity regularization parameters. The stability of interesting components extraction from fourteen subjects’ dat…