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.
This paper studies the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for segmentation of brain abnormality in MRI images. Segmentation of MRI image is an important part of brain imaging research. In this study, 150 MRI images were used as testing data for the system. The data was created by combining the shapes and size of various abnormalities and pasting it onto normal brain image...
Segmentation is a difficult task and challenging problem in the brain medical images for diagnosing cancer portion and other brain related diseases. Many researchers have introduced various segmentation techniques for brain medical images, however fuzzy clustering based fuzzy c-means image segmentation technique is more effective compared to other segmentation techniques. This paper introduces three...
This work aims at creating a structural atlas for brain MR images, which would help to solve clinical problems, faced during the training periods and can also be referred as a data set for medical diagnosis. Medical images taken as inputs are correlated with predefined atlas image for diagnosing the presence of anomalies. The images are segmented and labeled by using various techniques like thresholding,...
Brain connectivity studies aim at describing the connections within the brain. Diffusion and functional MRI techniques provide different kinds of information to understand brain connectivity non-invasively. Fiber tract segmentation is the task of identifying pathways of neuronal axons connecting different brain areas from MRI data. In this work we propose a method to investigate the role of both diffusion...
Magnetic resonance imaging (MRI) is a widely used method to obtain high quality medical image of the brain. Post-processing MR images with segmentation algorithms enhances the visualization and measurement of soft tissues and lesions. However, the conventional algorithms are not perfect and there are still some regions which are not partitioned accurately. In this paper, a new ant colony algorithm...
Accurate segmentation of magnetic resonance images according to tissue type is widely studded by many researcher, Recently Markov Random Field (MRF) has been used in this area. However the original MRF is supervised. So we introduce a novel approach called Dirichlet Markov Random Field for Magnetic Resonance Image (MRI) brain tissue classification. The approach uses Dirchilet Process Mixture (DPM)...
Thalamus and its interaction with cerebral cortex are considered essential in the generation and propagation of spike and wave discharge (SWD). Our objective is to evaluate the spontaneous functional connectivity between the thalamus and cerebral cortex in absence epilepsy. We use the resting-state fMRI to determine the whole brain functional connectivity with the mediodorsal thalamic nucleus (MDTN)...
MRI studies in post-traumatic stress disorder (PTSD) have focused primarily on manually-based hippocampal volumetry. However, the variation in cortical thickness and the relationship between the cortical volume and thickness alteration has not been well investigated in PTSD. In this paper, Laplacian method was first utilized to estimate cortical thickness after automatic segmentation of grey matter...
Noise confounds present serious complications to accurate data analysis in functional magnetic resonance imaging (fMRI). Simply relying on contextual image information often results in unsatisfactory segmentation of active brain regions. To remedy this, we propose a novel Group Markov Random Field (Group MRF) that extends the neighborhood system to other subjects to incorporate group information in...
Subtle changes in brain tissue that reflect the pathological processes of disorders such as mild cognitive impairment (MCI) are much more difficult to observe on a patient's magnetic resonance imaging (MRI) scan than those obvious abnormalities such as large strokes or tumors. Thus, it is necessary to develop an automated computer-aided diagnosis (CAD) system which will be more efficient and accurate...
A novel method is presented for automatic identification of the anterior commissure (AC) and posterior commissure (PC) in T2-weighted MR volumetric images (MRI). AC and PC are two critical landmarks of human brain. It is important to accurately identify them for brain segmentation, registration, functional neurosurgery, human brain mapping, and particularly for the Talairach transformation. The algorithm...
In this study, we present a systematic method for early detection of mild cognitive impairment (MCI) from magnetic resonance images (MRI) using image differences and clinical features. Early detection of MCI has pivotal importance to delay or prevent the onset of Alzheimer's disease (AD). Subjects were selected from the Open Access Series of Imaging Studies (OASIS)database and included 89 MCI subjects...
In MRI images, the boundary of each encephalic tissue is highly irregular. Sphere-Shaped Support Vector Machine (SSSVM) has the advantage of solving high non-linearity and irregularity problems. In this paper, SSSVM is applied in brain MRI image 3D reconstruction. Selecting parameters for SSSVM, however, is a complicated problem. Appropriate parameters can make the model more flexible and help to...
A Brain Cancer Detection and Classification System has been designed and developed. The system uses computer based procedures to detect tumor blocks or lesions and classify the type of tumor using Artificial Neural Network in MRI images of different patients with Astrocytoma type of brain tumors. The image processing techniques such as histogram equalization, image segmentation, image enhancement,...
A field source reconstruction of the dipoles modeling the activated area of the brain while a subject performs the task of the voluntary motion of the hand is solved. Experimental data resulting from the integration of both EEG and fMRI are used.
We present the use of multiscale Amplitude Modulation Frequency Modulation (AM-FM) methods for analyzing brain white matter lesions that are associated with disease progression. We analyze lesions and normal appearing white matter (NAWM) longitudinally (0 and 6 months) and also for progression of disease. We use the expanded disability status scale (EDSS) to assess disease progression. The findings...
Brain structural volumes can be used for automatically classifying subjects into categories like controls and patients. We aimed to automatically separate patients with temporal lobe epilepsy (TLE) with and without hippocampal atrophy on MRI, pTLE and nTLE, from controls, and determine the epileptogenic side. In the proposed framework 83 brain structure volumes are identified using multi-atlas segmentation...
The location, size and shape of Multiple Sclerosis (MS) lesions are often used to diagnose and track disease progression. In order to effectively compare lesions in MRI stacks for the same patient imaged at intervals, these stacks must be aligned. This automatic alignment method was designed to minimize modification of segmented pixel values. The aligned lesion stacks can be browsed independently...
The location, size and shape of Multiple Sclerosis (MS) lesions are often used to diagnose and track disease progression. In order to effectively compare lesions in MRI stacks for the same patient imaged at intervals, these stacks must be aligned. This automatic alignment method was designed to minimize modification of segmented pixel values. The aligned lesion stacks can be browsed independently...
Imaging genetics is a new field of neuroscience that blends methods from computational anatomy and quantitative genetics to identify genetic influences on brain structure and function. Here we analyzed brain MRI data from 372 young adult twins to identify cortical regions in which gray matter volume is influenced by genetic differences across subjects. Thickness maps, reconstructed from surface models...
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.