3,960 research outputs found
Geometry Processing of Conventionally Produced Mouse Brain Slice Images
Brain mapping research in most neuroanatomical laboratories relies on
conventional processing techniques, which often introduce histological
artifacts such as tissue tears and tissue loss. In this paper we present
techniques and algorithms for automatic registration and 3D reconstruction of
conventionally produced mouse brain slices in a standardized atlas space. This
is achieved first by constructing a virtual 3D mouse brain model from annotated
slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed
model generates ARA-based slice images corresponding to the microscopic images
of histological brain sections. These image pairs are aligned using a geometric
approach through contour images. Histological artifacts in the microscopic
images are detected and removed using Constrained Delaunay Triangulation before
performing global alignment. Finally, non-linear registration is performed by
solving Laplace's equation with Dirichlet boundary conditions. Our methods
provide significant improvements over previously reported registration
techniques for the tested slices in 3D space, especially on slices with
significant histological artifacts. Further, as an application we count the
number of neurons in various anatomical regions using a dataset of 51
microscopic slices from a single mouse brain. This work represents a
significant contribution to this subfield of neuroscience as it provides tools
to neuroanatomist for analyzing and processing histological data.Comment: 14 pages, 11 figure
Surface Reconstruction from Unorganized Point Cloud Data via Progressive Local Mesh Matching
This thesis presents an integrated triangle mesh processing framework for surface reconstruction based on Delaunay triangulation. It features an innovative multi-level inheritance priority queuing mechanism for seeking and updating the optimum local manifold mesh at each data point. The proposed algorithms aim at generating a watertight triangle mesh interpolating all the input points data when all the fully matched local manifold meshes (umbrellas) are found. Compared to existing reconstruction algorithms, the proposed algorithms can automatically reconstruct watertight interpolation triangle mesh without additional hole-filling or manifold post-processing. The resulting surface can effectively recover the sharp features in the scanned physical object and capture their correct topology and geometric shapes reliably. The main Umbrella Facet Matching (UFM) algorithm and its two extended algorithms are documented in detail in the thesis. The UFM algorithm accomplishes and implements the core surface reconstruction framework based on a multi-level inheritance priority queuing mechanism according to the progressive matching results of local meshes. The first extended algorithm presents a new normal vector combinatorial estimation method for point cloud data depending on local mesh matching results, which is benefit to sharp features reconstruction. The second extended algorithm addresses the sharp-feature preservation issue in surface reconstruction by the proposed normal vector cone (NVC) filtering. The effectiveness of these algorithms has been demonstrated using both simulated and real-world point cloud data sets. For each algorithm, multiple case studies are performed and analyzed to validate its performance
Fast extraction of neuron morphologies from large-scale SBFSEM image stacks
Neuron morphology is frequently used to classify cell-types in the mammalian cortex. Apart from the shape of the soma and the axonal projections, morphological classification is largely defined by the dendrites of a neuron and their subcellular compartments, referred to as dendritic spines. The dimensions of a neuronâs dendritic compartment, including its spines, is also a major determinant of the passive and active electrical excitability of dendrites. Furthermore, the dimensions of dendritic branches and spines change during postnatal development and, possibly, following some types of neuronal activity patterns, changes depending on the activity of a neuron. Due to their small size, accurate quantitation of spine number and structure is difficult to achieve (Larkman, J Comp Neurol 306:332, 1991). Here we follow an analysis approach using high-resolution EM techniques. Serial block-face scanning electron microscopy (SBFSEM) enables automated imaging of large specimen volumes at high resolution. The large data sets generated by this technique make manual reconstruction of neuronal structure laborious. Here we present NeuroStruct, a reconstruction environment developed for fast and automated analysis of large SBFSEM data sets containing individual stained neurons using optimized algorithms for CPU and GPU hardware. NeuroStruct is based on 3D operators and integrates image information from image stacks of individual neurons filled with biocytin and stained with osmium tetroxide. The focus of the presented work is the reconstruction of dendritic branches with detailed representation of spines. NeuroStruct delivers both a 3D surface model of the reconstructed structures and a 1D geometrical model corresponding to the skeleton of the reconstructed structures. Both representations are a prerequisite for analysis of morphological characteristics and simulation signalling within a neuron that capture the influence of spines
GNG based foot reconstruction for custom footwear manufacturing
Custom shoes manufacturing is one of the major challenges facing the footwear industry today. A shoe for everyone: it is a change in the production model in which each individualâs foot is the main focus, replacing traditional size systems based on population means. This paradigm shift represents a major effort for the industry, for which the design and not production becomes the main bottleneck. It is therefore necessary to accelerate the design process by improving the accuracy of current methods. The starting point for making a shoe that fits the clientâs foot anatomy is scanning the surface of the foot. Automated foot model reconstruction is accomplished through the use of the self-organising growing neural gas (GNG) network, which is able to topographically map the low dimension of the network to the high dimension of the manifold of the scanner acquisitions without requiring a priori knowledge of the structure of the input space. The GNG obtains a surface representation adapted to the topology of the foot, is accurate, tolerant to noise, and eliminates outliers. It also improves the reconstruction in âdarkâ areas where the scanner does not obtain information: the heel and toe areas. The method reconstructs the foot surface 4 times more accurately than other well-known methods. The method is generic and easily extensible to other industrial objects that need to be digitized and reconstructed with accuracy and efficiency requirements.This work was partially funded by the Spanish Government DPI2013-40534-R grant, supported with Feder funds, NILS Mobility Project 012-ABEL-CM-2014A, and FundaciĂłn SĂ©neca 18946/JLI/13
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
Gap Filling of 3-D Microvascular Networks by Tensor Voting
We present a new algorithm which merges discontinuities in 3-D images of tubular structures presenting undesirable gaps. The application of the proposed method is mainly associated to large 3-D images of microvascular networks. In order to recover the real network topology, we need to ïŹll the gaps between the closest discontinuous vessels. The algorithm presented in this paper aims at achieving this goal. This algorithm is based on the skeletonization of the segmented network followed by a tensor voting method. It permits to merge the most common kinds of discontinuities found in microvascular networks. It is robust, easy to use, and relatively fast. The microvascular network images were obtained using synchrotron tomography imaging at the European Synchrotron Radiation Facility. These images exhibit samples of intracortical networks. Representative results are illustrated
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