1,464 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
Spatial based Expectation Maximizing (EM)
<p>Abstract</p> <p>Background</p> <p>Expectation maximizing (EM) is one of the common approaches for image segmentation.</p> <p>Methods</p> <p>an improvement of the EM algorithm is proposed and its effectiveness for MRI brain image segmentation is investigated. In order to improve EM performance, the proposed algorithms incorporates neighbourhood information into the clustering process. At first, average image is obtained as neighbourhood information and then it is incorporated in clustering process. Also, as an option, user-interaction is used to improve segmentation results. Simulated and real MR volumes are used to compare the efficiency of the proposed improvement with the existing neighbourhood based extension for EM and FCM.</p> <p>Results</p> <p>the findings show that the proposed algorithm produces higher similarity index.</p> <p>Conclusions</p> <p>experiments demonstrate the effectiveness of the proposed algorithm in compare to other existing algorithms on various noise levels.</p
Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method
3D visualization plays an essential role in medical diagnosis and setting treatment plans especially for brain cancer. There have been many attempts for brain tumor reconstruction and visualization using various techniques. However, this problem is still considered unsolved as more accurate results are needed in this critical field. In this paper, a sequence of 2D slices of brain magnetic resonance Images was used to reconstruct a 3D model for the brain tumor. The images were automatically segmented using a wavelet multi-resolution expectation maximization algorithm. Then, the inter-slice gaps were interpolated using the proposed modified shape-based interpolation method. The method involves three main steps; transferring the binary tumor images to distance images using a suitable distance function, interpolating the distance images using cubic spline interpolation and thresholding the interpolated values to get the reconstructed slices. The final tumor is then visualized as a 3D isosurface. We evaluated the proposed method by removing an original slice from the input images and interpolating it, the results outperform the original shape-based interpolation method by an average of 3% reaching 99% of accuracy for some slice images
Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
Segmentation of both white matter lesions and deep grey matter structures is
an important task in the quantification of magnetic resonance imaging in
multiple sclerosis. Typically these tasks are performed separately: in this
paper we present a single segmentation solution based on convolutional neural
networks (CNNs) for providing fast, reliable segmentations of multimodal
magnetic resonance images into lesion classes and normal-appearing grey- and
white-matter structures. We show substantial, statistically significant
improvements in both Dice coefficient and in lesion-wise specificity and
sensitivity, compared to previous approaches, and agreement with individual
human raters in the range of human inter-rater variability. The method is
trained on data gathered from a single centre: nonetheless, it performs well on
data from centres, scanners and field-strengths not represented in the training
dataset. A retrospective study found that the classifier successfully
identified lesions missed by the human raters.
Lesion labels were provided by human raters, while weak labels for other
brain structures (including CSF, cortical grey matter, cortical white matter,
cerebellum, amygdala, hippocampus, subcortical GM structures and choroid
plexus) were provided by Freesurfer 5.3. The segmentations of these structures
compared well, not only with Freesurfer 5.3, but also with FSL-First and
Freesurfer 6.0
Heuristically improved Bayesian segmentation of brain MR images
One of the major tasks or even the most prevalent task in medical imageprocessing is image segmentation. Among them, brain MR images sufferfrom some difficulties such as intensity inhomogeneity of tissues, partialvolume effect, noise and some other imaging artifacts and so their segmentation is more challenging. Therefore, brain MRI segmentationbased on just gray values is prone to error. Hence involving problem specific heuristics and expert knowledge in designing segmentation algorithms seems to be useful. A two-phase segmentation algorithm basedon Bayesian method is proposed in this paper. The Bayesian part uses thegray value in segmenting images and the segmented image is used as theinput to the second phase to improve the misclassified pixels especially inborders between tissues. Similarity index is used to compare our algorithmwith the well known method of Ashburner which has been implemented inStatistical Parametric Mapping (SPM) package. Brainweb as a simulatedbrain MRI dataset is used in evaluating the proposed algorithm. Resultsshow that our algorithm performs well in comparison with the one implemented in SPM. It can be concluded that incorporating expert knowledge and problem specific heuristics improve segmentation result.The major advantage of proposed method is that one can update theknowledge base and incorporate new information into segmentationprocess by adding new rules.Keywords: Magnetic Resonance Imaging (MRI); Segmentation; Bayesianclassifier; Heuristic
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