Terrain Level of Detail

Luebke, David; Reddy, Martin; Cohen, Jonathan D.; Varshney, Amitabh; Watson, Benjamin; Huebner, Robert J.

doi:10.1016/b978-155860838-2/50009-1

Cited by 78 publications

(32 citation statements)

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“…An overview of these methods as they are applied to geometric models is provided by Luebke [19]. While these metrics are designed to evaluate two-dimensional surfaces, applying them to interconnected networks of one-dimensional centerlines is straightforward.…”

Section: Introductionmentioning

confidence: 99%

NetMets: software for quantifying and visualizing errors in biological network segmentation

et al. 2012

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One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization.

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Section: Introductionmentioning

confidence: 99%

NetMets: software for quantifying and visualizing errors in biological network segmentation

et al. 2012

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“…The most robust areas seems to be in the same places for a set of resolution. Inspired by the discrete LOD (Level Of Detail), 12,13 we will decimate the mesh in a lower resolution to synchronize the watermark in this new resolution domain.…”

Section: Resultsmentioning

confidence: 99%

Finding robust vertices for 3D synchronization based on Euclidean minimum spanning tree

et al. 2011

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Synchronization in 3D data hiding is one of the main problems. We need to know where we can embed information, and be able to find this space in order to extract the message. Various algorithms propose synchronization techniques by triangle or vertex path in a 3D mesh. In this paper, we proposed a new synchronization technique based on Euclidean minimum spanning tree computing (EMST) and the analysis of the displacement of the vertices without moving the connections in the tree. Based on the analysis of the vertices, we select the most robust vertices and synchronize these areas by computing a new EMST called "robust EMST". Then, we analyze the robustness of the technique, i.e. the stability of the most robust vertices selection; and demonstrate the consistence of the criterion selection with the vertex displacement.

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“…Multiresolution approaches (Clark, 1976; Luebke et al, 2002) have been traditionally used in these kinds of situations, due to their ability to manage different representations of the same objects in a given scene, selecting the most appropriate representation in each case according to different criteria (Luebke et al, 2002). This approach has been followed in methods for neural membrane CPU mesh generation with different levels of detail (Brito et al, 2013), where the mesh resolution is fixed through the specification of the number of sections and cross sections for each segment of the morphological tracing, and (Lasserre et al, 2012), where different levels of detail are obtained through consecutive application of the Catmull–Clark (Catmull and Clark, 1978) subdivision algorithm.…”

Section: State Of the Artmentioning

confidence: 99%

NeuroTessMesh: A Tool for the Generation and Visualization of Neuron Meshes and Adaptive On-the-Fly Refinement

García-Cantero

Brito

Mata

et al. 2017

Front. Neuroinform.

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Gaining a better understanding of the human brain continues to be one of the greatest challenges for science, largely because of the overwhelming complexity of the brain and the difficulty of analyzing the features and behavior of dense neural networks. Regarding analysis, 3D visualization has proven to be a useful tool for the evaluation of complex systems. However, the large number of neurons in non-trivial circuits, together with their intricate geometry, makes the visualization of a neuronal scenario an extremely challenging computational problem. Previous work in this area dealt with the generation of 3D polygonal meshes that approximated the cells’ overall anatomy but did not attempt to deal with the extremely high storage and computational cost required to manage a complex scene. This paper presents NeuroTessMesh, a tool specifically designed to cope with many of the problems associated with the visualization of neural circuits that are comprised of large numbers of cells. In addition, this method facilitates the recovery and visualization of the 3D geometry of cells included in databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma’s morphology. This method takes as its only input the available compact, yet incomplete, morphological tracings of the cells as acquired by neuroscientists. It uses a multiresolution approach that combines an initial, coarse mesh generation with subsequent on-the-fly adaptive mesh refinement stages using tessellation shaders. For the coarse mesh generation, a novel approach, based on the Finite Element Method, allows approximation of the 3D shape of the soma from its incomplete description. Subsequently, the adaptive refinement process performed in the graphic card generates meshes that provide good visual quality geometries at a reasonable computational cost, both in terms of memory and rendering time. All the described techniques have been integrated into NeuroTessMesh, available to the scientific community, to generate, visualize, and save the adaptive resolution meshes.

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Terrain Level of Detail

Cited by 78 publications

References 0 publications

NetMets: software for quantifying and visualizing errors in biological network segmentation

NetMets: software for quantifying and visualizing errors in biological network segmentation

Finding robust vertices for 3D synchronization based on Euclidean minimum spanning tree

NeuroTessMesh: A Tool for the Generation and Visualization of Neuron Meshes and Adaptive On-the-Fly Refinement

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