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Brain development is a protracted and dynamic process. Many studies have charted the trajectory of white matter development, but here we sought to map these effects in greater detail, based on a large set of fiber tracts automatically extracted from HARDI (high angular resolution diffusion imaging) at 4 tesla. We used autoMATE (automated multi-atlas tract extraction) to extract diffusivity measures...
The most widely used classification techniques for whole brain image classification rely on kernel machines such as support vector machines and Gaussian processes, due to their computational efficiency, accurate prediction and suitability to tackle the combination of small sample sizes and high dimensionality that make neuroimaging data a challenging problem. Such methods generally make use of linear...
Despite ongoing improvements in magnetic resonance (MR) imaging (MRI), considerable clinical and, to a lesser extent, research data is acquired at lower resolutions. For example 1 mm isotropic acquisition of T1-weighted (T1-w) Magnetization Prepared Rapid Gradient Echo (MPRAGE) is standard practice, however T2-weighted (T2-w) — because of its longer relaxation times (and thus longer scan time) — is...
The popular NL-means denoising algorithm proposes to modify the intensity of each voxel of an image by a weighted sum of the intensities of similar voxels. The success of the NL-means rests on the fact that there are typically enough such similar voxels in natural, and even medical images; in other words, that there is some self-similarity/redundancy in such images. However, similarity between voxels...
Advances in fast 2D MRI have led to its growing clinical use in un-sedated fetal brain studies, as a tool for challenging neurodevelopmental cases. The availability of this 2D data has motivated new engineering developments that combine fast multi-slice MRI scans with computer vision techniques to provide a route to full 3D fetal brain image formation in a significant fraction of imaging studies....
This paper presents how using a correspondence-based interpolation scheme for 3D image registration improves the registration accuracy. The interpolator takes into account correspondences across slices, which is an advantage, particularly when the volume has thick slices, and where anatomies lie non-parallel to the slice direction. We use our previously presented approach for correspondence-based...
To achieve high temporal and spatial resolutions in Magnetic Resonance (MR) imaging, one of the keys is to improve the signal-to-noise ratio (SNR) at the signal receiving stage. We used ultra high-field at 11.7 Tesla and custom surface array engineered to fit the size and geometry of a mouse brain in order to increase the SNR and to aim for very high spatial resolution. The coil array was a receive-only...
The expectation-maximization (EM) algorithm has been widely applied to the estimation of Gaussian mixture model (GMM) in brain MR image segmentation. However, the EM algorithm is deterministic and intrinsically prone to overfitting the training data and being trapped in local optima. In this paper, we propose a hybrid genetic and variational EM (GA-VEM) algorithm for brain MR image segmentation. In...
Implementation of a neuro-fuzzy segmentation process of the MRI data is presented in this study to detect various tissues like white matter, gray matter, csf and tumor. The advantage of hierarchical self organizing map and fuzzy c means algorithms are used to classify the image layer by layer. The lowest level weight vector is achieved by the abstraction level. We have also achieved a higher value...
This paper examines new techniques that allow the formation and analysis of high resolution 3D MR images of the developing human fetal brain in utero, by carrying out object based motion correction of multi-slice MR images of the moving fetal head within deforming maternal tissues. The approaches combine modern fast snapshot slice imaging with techniques derived from computer vision to retrospectively...
We present a unified framework for data processing, mining and interactive visualization of large-scale neuroanatomical databases. The input data is assumed to lie in a specific atlas space, or simply exist as a separate collection. Users can specify their own atlas for comparative analyses. The original data exist as MRI images in standard formats. It is uploaded to a remote server and processed...
This paper presents a generalization of the Bowsher prior for SPECT reconstruction using anatomical prior. Instead of considering a binary selection of the neighbors of each reconstructed voxel based on the anatomical prior values, each neighbors are taken into account with a suitable weight. We tried three different weights. We showed that, for brain SPECT reconstruction using MRI, in the case of...
In the field of quantitative imaging, the creation of accurate volumes of interest (VOIs) is often of central importance. However, the process of creating these VOIS for multiple subjects can be time-intensive and there are many chances to introduce variability on inter- and intra-investigator levels. Although previous work has shown that image normalization through cortical surface mapping can be...
A novel MR image acquisition protocol has been presented in order to obtain high resolution image of the brain at acquisition time. For better delineation of the substructures of the brain we have emphasized enhancing the intensity difference between white and gray matters in the reconstructed MR image of the brain. The mathematical basis of the protocol stems from the T1 weighting combined with an...
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...
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 magnetic resonance (MR) images consist of different structures and features when they are observed at different scales and layers. Conventional non-rigid brain MR image registration methods mainly estimate the optimum transformation by relying on the information of a single layer and this can lead to the loss of information contained in other layers. In this paper, we propose a multi-layer framework...
In this paper, a new algorithm for MRI Brain Segmentation is proposed, which is based on the AntPart algorithm [1]. This algorithm proposed partitiones the brain structure into three parts-white matter, grey matter, and cerebrospinal fluid according to the grayvalues of pixels. The main algorithm compares each pixel with the nearest class center C, all the data belonging to class C, all the data carried...
Brain volume segmentation from neonatal magnetic resonance images (MRI) offers the possibility of exploring the developmental changes, measuring the brain growth, detecting early disorders and three-dimensional (3D) volume reconstruction. However, such segmentation is challenging mainly due to the fast growth process, complex anatomy of the developing brain and often poor MRI quality. Existing techniques...
The bio-imaging techniques have widespread applications from diagnosing diseases to investigating the body tissues at the cells level. Traditionally, these techniques were used mainly in the orthopedic treatment. However, with the development of infrared cameras, ultrasound, and radio wave technology, they are used in different medical fields such as cardiovascular analysis, neurological treatment...
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