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Dyslexia severely impairs learning abilities of children, so that improved diagnostic methods are needed. Neuropathological studies have revealed an abnormal anatomy of the Corpus Callosum (CC) in dyslexic brains. We propose a new approach to quantitative analysis of three-dimensional (3D) magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences...
Diffusion MRI (DMRI) signal is characterized by the self-diffusion propagation profile within the brain white matters. Despite previous efforts on quantification of this physical phenomenon in the literature, most existing methods suffer from a number of constraints which severely limit the extent of their practical applicability. In this work, we relax these limitations to a large degree by addressing...
Magnetic resonance imaging of the brain at high fields (e.g. 3T) provides high resolution and high signal to noise ratio images suitable for a wide range of clinical applications. However, radiofrequency (or B1) inhomogeneity increases with the magnetic field and produces undesired intensity variations responsible for inaccuracies in quantitative analyses. A method to perform brain segmentation using...
This paper presents a novel classification via aggregated regression algorithm - dubbed CAVIAR - and its application to the OASIS MRI brain image database. The CAVIAR algorithm simultaneously combines a set of weak learners based on the assumption that the weight combination for the final strong hypothesis in CAVIAR depends on both the weak learners and the training data. A regularization scheme using...
It is an important task to automatically segment brain anatomical structures from 3D MRI images. One major challenge in this problem is to learn/design effective models, for both intensity appearances and shapes, accounting for the large image variation due to the acquisition processes by different machines, at different parameters, and for different subjects. Generative models study the explicit...
Structural connectivity in human brain has been studied by modeling the statistical dependence between features of cortical regions, such as gray matter thickness. Statistical correlations between gray matter thickness have been mainly used as a metric to study this dependence. In this paper, we propose the use of partial correlations instead of Pearson correlation for inferring the brain structural...
Image super-resolution techniques provide a route to studying fine scale anatomical detail using one or more lower resolution acquisitions. A crucial issue in such algorithms is the form of image regularization used to constrain the image structure at points where there are insufficient data values. In this paper we examine the specific problem of reconstructing a high resolution isotropic image when...
Brain tumor segmentation is an important image processing step in diagnosis, treatment planning, and follow-up studies of Glioblastoma (GBM). However it is still a challenging task due to varying in size, shape, location, and image intensities within and around the tumor. In this paper, we propose a new brain tumor segmentation method for T1-weighted MR brain images based on an improved level set...
We present an analytical form of ground-truth k-space data for the 2-D Shepp-Logan brain phantom in the presence of multiple and non-homogeneous receiving coils. The analytical form allows us to conduct realistic simulations and validations of reconstruction algorithms for parallel MRI. The key contribution of our work is to use a polynomial representation of the coil's sensitivity. We show that this...
Probabilistic tractography has emerged as an alternative to classical deterministic methods to overcome their lack of connectivity information between different brain regions. However, it relies on statistical sampling, which is computationally taxing. In this study, a well-known, random walk based stochastic tractography method is discretized by limiting the set of directions that a sampling particle...
It is generally accepted that the completeness of resection in malignant gliomas should be as complete as possible. Maximal cytoreductive surgery is generally performed aiming at removing at least that part of the tumor that accumulates a contrast agent for magnetic resonance imaging (contrast-enhanced MRI) (1;2). Complete resection of contrast enhancing tumor regions as judged by post-operative MRI...
This paper presents an automatic method for the segmentation of Optic Pathway Gliomas (OPGs) from multi-spectral MRI datasets. The method starts with the automatic localization of the OPG and its core with an anatomical tumor atlas followed by a binary voxel classification with a probabilistic tissue model whose parameters are estimated from MR images. The method effectively incorporates prior location,...
This work proposes a multifractal analysis of the time series derived from ASL fMRI (Arterial Spin Labeling functional Magnetic Resonance Imaging) to detect brain activated regions in response to an unknown stimulus. In contrast to standard model-based activation analysis, no prior knowledge of the expected haemodynamic response has to be assumed for extracting activation patterns from fMRI. The ASL...
There are three parts in MRI projections of which are grey matter formed basically by neurons, white matter formed by axon extremities with mylelins, and cerebrospinal fluid. Changes and damages in these regions can cause various diseases. Autism, Parkinsonism, dyslexia, mental disorders, visual and audial loss can be the examples of grey matter diseases. As for the white matter diseases, MS (multiple...
In this study, we analyzed the MR spectroscopic and MR diffusion weighted imaging parameter differences among the subtypes of grade 3 brain tumor patients (anaplastic astrocytoma, oligodendroglioma, and oligoastrocytoma). We observed higher lipid values in the tumor regions of oligodendrogliomas than both astrocytomas and oligoastrocytomas. According to these results, there were not significant MR...
Using Hebbian learning rule and its special case Self-Organizing Map (SOM) as unsupervised learning, a solution is proposed for defining the fiber paths which is a critical problem in diffusion tensor literature, and synthetic diffusion patterns are analyzed by artificial neural network (ANN) approach. Unsupervised learning in training neural networks is a method, where network classification rules...
Major depressive disorder (MDD) is one of the most common affective disorders which ranks among the top causes of worldwide disease burden and disability. Recent studies suggested that MDD resulted in the functional connectivity alteration in the resting state brain networks, such as the emotional circuit and the default mode network (DMN). In addition to these emotion-relevant networks, some other...
We present a novel methodological framework for leveraging multiple image sources, including different modalities, acquisition protocols or image features, in the registration of more than two images via information theoretic data fusion. The technique, referred to as multi-attribute combined mutual information (MACMI), adopts a multivariate application of mutual information (MI) to allow several...
In this paper we propose a variational approach for multimodal image registration based on the diffeomorphic demons algorithm. Diffeomorphic demons has proven to be a robust and efficient way for intensity-based image registration. However, the main drawback is that it cannot deal with multiple modalities. We propose to replace the standard demons similarity metric (image intensity differences) by...
Although thalamic nuclei are not directly visible on conventional anatomical magnetic resonance images (MRI), it is possible to observe differences between the nuclei using diffusion tensor imaging (DTI), because of their distinct fiber orientation. This work presents a method to segment the various nuclei of human thalamus using diffusion MRI. Our approach is to use the watershed transform and other...
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