3,945 research outputs found
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
A Probabilistic Adaptive Cerebral Cortex Segmentation Algorithm for Magnetic Resonance Human Head Scan Images
The total efficiency of Magnetic Resonance Imaging (MRI) results in the need for human involvement in order to appropriately detect information contained in the image. Currently, there has been a surge in interest in automated algorithms that can more precisely divide medical image structures into substructures than prior attempts. Instant segregation of cerebral cortex width from MRI scanned images is difficult due to noise, Intensity Non-Uniformity (INU), Partial Volume Effects (PVE), MRI's low resolution, and the very complicated architecture of the cortical folds. In this paper, a Probabilistic Adaptive Cerebral Cortex Segmentation (PACCS) approach is proposed for segmenting brain areas of T1 weighted MRI of human head images. Skull Stripping (SS), Brain Hemisphere Segmentation (BHS) and CCS are the three primary processes in the suggested technique. In step 1, Non-Brain Cells (NBC) is eliminated by a Contour-Based Two-Stage Brain Extraction Method (CTS-BEM). Step 2 details a basic BHS technique for Curve Fitting (CF) detection in MRI human head images. The left and right hemispheres are divided using the discovered Mid-Sagittal Plane (MSP). At last, to enhance a probabilistic CCS structure with adjustments such as prior facts change to remove segmentation bias; the creation of express direct extent training; and a segmentation version based on a regionally various Gaussian Mixture Model- Hidden Markov Random Field – Expectation Maximization (GMM-HMRF-EM). The underlying partial extent categorization and its interplay with found image intensities are represented as a spatially correlated HMRF within the GMM-HMRF-EM method. The proposed GMM-HMRF method estimates HMRF parameters using the EM technique. Finally, the outcomes of segmentation are evaluated in terms of precision, recall, specificity, Jaccard Similarity (JS), and Dice Similarity (DS). The proposed method works better and more consistently than the present locally Varying MRF (LV-MRF), according to the experimental findings obtained by using the suggested GMM-HMRF-EM methodology to 18 individuals' brain images
Combining global and local information for the segmentation of MR images of the brain
Magnetic resonance imaging can provide high resolution volumetric images of the brain with exceptional soft tissue contrast. These factors allow the complex structure of the brain to be clearly visualised. This has lead to the development of quantitative methods to analyse neuroanatomical structures. In turn, this has promoted the use of computational methods to automate and improve these techniques. This thesis investigates methods to accurately segment MRI images of the brain. The use of global and local image information is considered, where global information includes image intensity distributions, means and variances and local information is based on the relationship between spatially neighbouring voxels. Methods are explored that aim to improve the classification and segmentation of MR images of the brain by combining these elements. Some common artefacts exist in MR brain images that can be seriously detrimental to image analysis methods. Methods to correct for these artifacts are assessed by exploring their effect, first with some well established classification methods and then with methods that combine global information with local information in the form of a Markov random field model. Another characteristic of MR images is the partial volume effect that occurs where signals from different tissues become mixed over the finite volume of a voxel. This effect is demonstrated and quantified using a simulation. Analysis methods that address these issues are tested on simulated and real MR images. They are also applied to study the structure of the temporal lobes in a group of patients with temporal lobe epilepsy. The results emphasise the benefits and limitations of applying these methods to a problem of this nature. The work in this thesis demonstrates the advantages of using global and local information together in the segmentation of MR brain images and proposes a generalised framework that allows this information to be combined in a flexible way
Segmentation of brain MRI during early childhood
The objective of this thesis is the development of automatic methods to measure the changes in
volume and growth of brain structures in prematurely born infants. Automatic tools for accurate
tissue quantification from magnetic resonance images can provide means for understanding
how the neurodevelopmental effects of the premature birth, such as cognitive, neurological or
behavioural impairment, are related to underlying changes in brain anatomy. Understanding
these changes forms a basis for development of suitable treatments to improve the outcomes of
premature birth.
In this thesis we focus on the segmentation of brain structures from magnetic resonance images
during early childhood. Most of the current brain segmentation techniques have been focused
on the segmentation of adult or neonatal brains. As a result of rapid development, the brain
anatomy during early childhood differs from anatomy of both adult and neonatal brains and
therefore requires adaptations of available techniques to produce good results.
To address the issue of anatomical differences of the brain during early childhood compared
to other age-groups, population-specific deformable and probabilistic atlases are introduced. A
method for generation of population-specific prior information in form of a probabilistic atlas
is proposed and used to enhance existing segmentation algorithms.
The evaluation of registration-based and intensity-based approaches shows the techniques to
be complementary in the quality of automatic segmentation in different parts of the brain. We
propose a novel robust segmentation method combining the advantages of both approaches. The
method is based on multiple label propagation using B-spline non-rigid registration followed by
EM segmentation.
Intensity inhomogeneity is a shading artefact resulting from the acquisition process, which
significantly affects modern high resolution MR data acquired at higher magnetic field strengths.
A novel template based method focused on correcting the intensity inhomogeneity in data
acquired at higher magnetic field strengths is therefore proposed.
The proposed segmentation method combined with proposed intensity inhomogeneity correction
method offers a robust tool for quantification of volumes and growth of brain structures during
early childhood. The tool have been applied to 67 T1-weigted images of subject at one and two years of age
A Review on MR Image Intensity Inhomogeneity Correction
Intensity inhomogeneity (IIH) is often encountered in MR imaging,
and a number of techniques have been devised to correct this
artifact. This paper attempts to review some of the recent
developments in the mathematical modeling of IIH field.
Low-frequency models are widely used, but they tend to corrupt the
low-frequency components of the tissue. Hypersurface models and
statistical models can be adaptive to the image and generally more
stable, but they are also generally more complex and consume more
computer memory and CPU time. They are often formulated together
with image segmentation within one framework and the overall
performance is highly dependent on the segmentation process.
Beside these three popular models, this paper also summarizes
other techniques based on different principles. In addition, the
issue of quantitative evaluation and comparative study are
discussed
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
Parallel MRI is a fast imaging technique that enables the acquisition of
highly resolved images in space or/and in time. The performance of parallel
imaging strongly depends on the reconstruction algorithm, which can proceed
either in the original k-space (GRAPPA, SMASH) or in the image domain
(SENSE-like methods). To improve the performance of the widely used SENSE
algorithm, 2D- or slice-specific regularization in the wavelet domain has been
deeply investigated. In this paper, we extend this approach using 3D-wavelet
representations in order to handle all slices together and address
reconstruction artifacts which propagate across adjacent slices. The gain
induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE:
3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal
acquisition is considered. Another important extension accounts for temporal
correlations that exist between successive scans in functional MRI (fMRI). In
addition to the case of 2D+t acquisition schemes addressed by some other
methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition
schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and
4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that
all regularization parameters are estimated in the maximum likelihood sense on
a reference scan. The gain induced by such extensions is illustrated on both
anatomical and functional image reconstruction, and also measured in terms of
statistical sensitivity for the 4D-UWR-SENSE approach during a fast
event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE
reconstruction at the subject and group levels (15 subjects) for different
contrasts of interest (eg, motor or computation tasks) and using different
parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353
- …