232,694 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
Critical Thickness Ratio for Buckled and Wrinkled Fruits and Vegetables
Fruits and vegetables are usually composed of exocarp and sarcocarp and they
take a variety of shapes when they are ripe. Buckled and wrinkled fruits and
vegetables are often observed. This work aims at establishing the geometrical
constraint for buckled and wrinkled shapes based on a mechanical model. The
mismatch of expansion rate between the exocarp and sarcocarp can produce a
compressive stress on the exocarp. We model a fruit/vegetable with exocarp and
sarcocarp as a hyperelastic layer-substrate structure subjected to uniaxial
compression. The derived bifurcation condition contains both geometrical and
material constants. However, a careful analysis on this condition leads to the
finding of a critical thickness ratio which separates the buckling and
wrinkling modes, and remarkably, which is independent of the material
stiffnesses. More specifically, it is found that if the thickness ratio is
smaller than this critical value a fruit/vegetable should be in a buckling mode
(under a sufficient stress); if a fruit/vegetable in a wrinkled shape the
thickness ratio is always larger than this critical value. To verify the
theoretical prediction, we consider four types of buckled fruits/vegetables and
four types of wrinkled fruits/vegetables with three samples in each type. The
geometrical parameters for the 24 samples are measured and it is found that
indeed all the data fall into the theoretically predicted buckling or wrinkling
domains. Some practical applications based on this critical thickness ratio are
briefly discussed.Comment: 11 pages 9 figures 2 table
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