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.
Alzheimer's disease (AD) is the most common progressive neurodegenerative disorder. Therefore, early detection and evaluation of prognosis of AD is an important issue in contemporary brain research. Magnetic Resonance Imaging (MRI) provides valuable diagnostic information about AD. In this work, brain tissue is extracted using phase-based level set method. Structure tensor analysis is used to visualize...
This paper reveals a computer aided system aimed at automatically segmenting brain tumors from MRI images is proposed, using bounding box-Active contour and Random walker algorithm. This method performs fast and accurate segmentation results by segmenting the most dissimilar regions of a tumor image. This paper overviews the benefits of automatic segmentation than the manual segmentation algorithms...
Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing. MRI (Magnetic Resonance Imaging) is a medical technique, mainly used by the radiologist for visualization of internal structure of the human body without any surgery. MRI provides plentiful information about the human soft tissue, which helps in the diagnosis of brain tumour....
Brain structure segmentation from 3D magnetic resonances image (MRI) allows supporting the analysis of physiological and pathological processes. Nonetheless, finding MRI relationships posses a challenge when analyzing in voxel-based high-dimensional spaces. We introduce a kernel-based representation approach to support MRI discrimination. In this sense, inherent Inter-Slice Kernel (ISK) relationship...
It is hard to segment the magnetic resonance image which has Gaussian noise. In this paper, we presented a novel approach for segmentation of brain MRI data. It could improve the accuracy and robustness of segmentation. First of all, a new energy function based on characteristics of brain data and Gaussian noise distribution was derived. Then, in order to obtain the stable segmentation results, active...
The definitive diagnosis of the type of epilepsy, if it exists, in medication-resistant seizure disorder is based on the efficient combination of clinical information, long-term video-electroencephalography (EEG) and neuroimaging. Diagnoses are reached by a consensus panel that combines these diverse modalities using clinical wisdom and experience. Here we compare two methods of multimodal computer-aided...
To date, there are no reliable markers for making an early diagnosis of schizophrenia before clinical diagnostic criteria are fully met. Neuroimaging and pattern classification techniques are promising tools towards predicting transition to schizophrenia. Here, we investigated the diagnostic performance of a combination of neuroanatomical and clinical data in predicting transition to schizophrenia...
There is growing interest among smartphone users in the ability to determine their precise location in their environment for a variety of applications related to way finding, travel and shopping. While GPS provides valuable self-localization estimates, its accuracy is limited to approximately 10 meters in most urban locations. This paper focuses on the self-localization needs of blind or visually...
Automated detection of brain pathologies from Magnetic Resonance (MR) images remains an outstanding problem. An information theoretic approach for automated segmentation of medical images called the Improved "Jump" Method (IJM) has been recently developed and validated. Here we extend this work by utilizing IJM to segment human brain MR images with multiple-sclerosis (MS) lesions in order...
The objective of this work is to develop and validate a computational method for fast and globally solving the registration problem of the three-dimensional (3-D) bone surface from MR images of knee joint. This technique is used to monitor the local cartilage thickness changes over time. By introducing a "Lipschitzation" process to the traditional cost function, global optimality is guaranteed...
The MRI or CT scan images are primary follow up diagnostic tools when a neurologic exam indicates a possibility of a primary or metastatic brain tumor existence. The tumor tissue mainly appears in brighter colors than the rest of the regions in the brain. Based on this observation, an automated algorithm for brain tumor detection and medical doctors' assistance in facilitated and accelerated diagnosis...
Wild bootstrap resampling technique was proposed to improve parameter estimations of intra-voxel incoherent motion (IVIM) MRI, i.e. diffusion fraction (f), diffusion (D) and pseudo-diffusion (D∗), without increasing scan time. It was verified via simulation and clinical scan. In simulation, estimation accuracy and uncertainty obtained from asymptotic fitting with and without wild bootstrapping were...
We propose a fully-automatic morphometric encoding targeted towards differentiating diseased from healthy cardiac MRI. Existing encodings rely on accurate segmentations of each scan. Segmentation generally includes labour-intensive editing and increases the risk associated with intra- and inter-rater variability. Our morphometric framework only requires the segmentation of a template scan. This template...
Diffusion MRI (dMRI) offers new signals for disease classification not available using standard anatomical MRI. However, most studies transform the raw signal to a parametric model before extracting features for classification. Here, we employ a novel method that models the signal directly to extract features for classification of Alzheimer's disease (AD) patients versus healthy controls (HC). We...
Multi-voxel pattern analysis is an approach to investigating brain activity measured by functional Magnetic Resonance Imaging (fMRI) in response to given stimuli. The signal acquired using fMRI is spatiotemporal, and can be used to predict the stimuli causing brain activation. Existing prediction methods suffer from the ‘curse of dimensionality’ by embedding all time points of the experiment in feature...
Graph theory is increasingly used in the field of neuroscience to understand the large-scale network structure of the human brain. There is also considerable interest in applying machine learning techniques in clinical settings, for example, to make diagnoses or predict treatment outcomes. Here we used support-vector machines (SVMs), in conjunction with whole-brain tractography, to identify graph...
Automatic detection of the fetal brain in Magnetic Resonance (MR) Images is especially difficult due to arbitrary orientation of the fetus and possible movements during the scan. In this paper, we propose a method to facilitate fully automatic brain voxel classification by means of rotation invariant volume descriptors. We calculate features for a set of 50 prenatal fast spin echo T2 volumes of the...
In this work, the feasibility of classifying amnestic mild cognitive impairment (aMCI), a prodromal stage of Alzheimer's disease, was investigated using fMRI activation patterns in the medial temporal lobes (MTL). The activation volume or relative activation extent in each of fourteen subregions of the MTL, when subjects were performing memory tasks, served as features for radial basis function networks...
In image-guided neurosurgery, preoperatively acquired diagnostic images (e.g., brain MRI) should be accurately registered to the physical space that is specific to the patient's intraoperative neuroanatomy. A popular framework of registration requires manual defining corresponding positions of fiducial markers on the patient head and the preoperative brain MRI. The procedure is time-consuming and...
Reliable detection of the mid-sagittal plane is the key for brain image registration, asymmetry analysis, and group studies. Although the brain presents most of the time a regular structure, outliers in the data consisting of brain tumors or various deformations pose challenges to the existing approaches. We propose in this paper a robust approach for mid-sagittal plane extraction based on hierarchical...
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.