6,984 research outputs found
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
Quantifying loopy network architectures
Biology presents many examples of planar distribution and structural networks
having dense sets of closed loops. An archetype of this form of network
organization is the vasculature of dicotyledonous leaves, which showcases a
hierarchically-nested architecture containing closed loops at many different
levels. Although a number of methods have been proposed to measure aspects of
the structure of such networks, a robust metric to quantify their hierarchical
organization is still lacking. We present an algorithmic framework, the
hierarchical loop decomposition, that allows mapping loopy networks to binary
trees, preserving in the connectivity of the trees the architecture of the
original graph. We apply this framework to investigate computer generated
graphs, such as artificial models and optimal distribution networks, as well as
natural graphs extracted from digitized images of dicotyledonous leaves and
vasculature of rat cerebral neocortex. We calculate various metrics based on
the Asymmetry, the cumulative size distribution and the Strahler bifurcation
ratios of the corresponding trees and discuss the relationship of these
quantities to the architectural organization of the original graphs. This
algorithmic framework decouples the geometric information (exact location of
edges and nodes) from the metric topology (connectivity and edge weight) and it
ultimately allows us to perform a quantitative statistical comparison between
predictions of theoretical models and naturally occurring loopy graphs.Comment: 17 pages, 8 figures. During preparation of this manuscript the
authors became aware of the work of Mileyko at al., concurrently submitted
for publicatio
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
The Small World of Osteocytes: Connectomics of the Lacuno-Canalicular Network in Bone
Osteocytes and their cell processes reside in a large, interconnected network
of voids pervading the mineralized bone matrix of most vertebrates. This
osteocyte lacuno-canalicular network (OLCN) is believed to play important roles
in mechanosensing, mineral homeostasis, and for the mechanical properties of
bone. While the extracellular matrix structure of bone is extensively studied
on ultrastructural and macroscopic scales, there is a lack of quantitative
knowledge on how the cellular network is organized. Using a recently introduced
imaging and quantification approach, we analyze the OLCN in different bone
types from mouse and sheep that exhibit different degrees of structural
organization not only of the cell network but also of the fibrous matrix
deposited by the cells. We define a number of robust, quantitative measures
that are derived from the theory of complex networks. These measures enable us
to gain insights into how efficient the network is organized with regard to
intercellular transport and communication. Our analysis shows that the cell
network in regularly organized, slow-growing bone tissue from sheep is less
connected, but more efficiently organized compared to irregular and
fast-growing bone tissue from mice. On the level of statistical topological
properties (edges per node, edge length and degree distribution), both network
types are indistinguishable, highlighting that despite pronounced differences
at the tissue level, the topological architecture of the osteocyte canalicular
network at the subcellular level may be independent of species and bone type.
Our results suggest a universal mechanism underlying the self-organization of
individual cells into a large, interconnected network during bone formation and
mineralization
Modeling Brain Circuitry over a Wide Range of Scales
If we are ever to unravel the mysteries of brain function at its most
fundamental level, we will need a precise understanding of how its component
neurons connect to each other. Electron Microscopes (EM) can now provide the
nanometer resolution that is needed to image synapses, and therefore
connections, while Light Microscopes (LM) see at the micrometer resolution
required to model the 3D structure of the dendritic network. Since both the
topology and the connection strength are integral parts of the brain's wiring
diagram, being able to combine these two modalities is critically important.
In fact, these microscopes now routinely produce high-resolution imagery in
such large quantities that the bottleneck becomes automated processing and
interpretation, which is needed for such data to be exploited to its full
potential. In this paper, we briefly review the Computer Vision techniques we
have developed at EPFL to address this need. They include delineating dendritic
arbors from LM imagery, segmenting organelles from EM, and combining the two
into a consistent representation
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