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Automatic particle detection in digitized electron micrographs
High resolution structural analysis of biological complexes can be carried out by single particle electron microscopy where a large number of particle images are available. Many approaches to automate the process of selection of particle positions from digitized electron micrograph images have been described, but so far none has proved as good as manual selection. This thesis describes a method which I have developed to locate such biological complexes by matching small boxed areas to a set of reference images using the radius of gyration, complemented by a series of other simple criteria. From the reference images, parameters such as the ratio between the average density of the central area and that in its surrounding band, and the density sum and variance are calculated. They are compared with corresponding values from a moving square window of densities extracted from the micrograph, and the coordinates of successfully matched candidate squares are recorded. Since the same particle is detected in a series of overlapping windows, candidates found to be within close proximity are grouped, and the best-fitting one is selected from each cluster. Along with a small stack of boxed reference images, a few specified parameter values, such as the particle radius and the minimum acceptable distance between particle centres are required to select the windows. Micrograph labels and other areas that do not contain appropriate specimens are automatically ignored in order to minimize false positives, and reduce the computing time. A computer program SLEUTH written to carry out this method of automatic particle detection includes a graphical user interface to assist the user in setting up the parameter values. The program has been tested successfully on a variety of different biological structures, from both negatively stained and ice-embedded specimens
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
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