Fast Marching 3D Reconstruction of Interphase Chromosomes

Matula, Pavel; Hubený, Jan; Kozubek, Michal

doi:10.1007/978-3-540-27816-0_33

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Malladi and Sethian [178] thus propose to use this evolution to segment object in noisy images, using a metric W (x) that is low for pixel x outside the object to detect, and using a radius t chosen to match the size of the object. See also [94,181] for applications of this method to medical imaging. Front freezing.…”

Section: Segmentation Using Geodesic Ballsmentioning

confidence: 99%

Geodesic Methods in Computer Vision and Graphics

Peyré¹

2009

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This paper reviews both the theory and practice of the numerical computation of geodesic distances on Riemannian manifolds. The notion of Riemannian manifold allows one to define a local metric (a symmetric positive tensor field) that encodes the information about the problem one wishes to solve. This takes into account a local isotropic cost (whether some point should be avoided or not) and a local anisotropy (which direction should be preferred). Using this local tensor field, the geodesic distance is used to solve many problems of practical interest such as segmentation using geodesic balls and Voronoi regions, sampling points at regular geodesic distance or meshing a domain with geodesic Delaunay triangles. The shortest paths for this Riemannian distance, the so-called geodesics, are also important because they follow salient curvilinear structures in the domain. We show several applications of the numerical computation of geodesic distances and shortest paths to problems in surface and shape processing, in particular segmentation, sampling, meshing and comparison of shapes.

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Section: Segmentation Using Geodesic Ballsmentioning

confidence: 99%

Geodesic Methods in Computer Vision and Graphics

Peyré¹

2009

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“…Recently, we have also tried several methods classified as deformable models. First, we have introduced so-called star-shaped simplex meshes (Matula and Svoboda, 2001) and then we have tested level-set methods (Matula et al, 2004).…”

Section: Image Analysismentioning

confidence: 99%

Automated acquisition and processing of multidimensional image data in confocal in vivo microscopy

Kozubek

Matula

et al. 2004

Microscopy Res & Technique

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The successful development of visualization techniques for live cell imaging leads to the development of suitable software for the acquisition and processing of multidimensional image data. This report compares several possible approaches to image acquisition and processing in confocal in vivo microscopy and suggests new alternatives to the published methods. Special attention is paid to spinning disk systems based either on a classical Nipkow disk or on the microlens principle. This study shows how to optimize image acquisition process in live cell studies using camera binning feature and how to perform object tracking using a new fast image registration method based on the graph theory.

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Improved 3D Reconstruction of Interphase Chromosomes Based on Nonlinear Diffusion Filtering

Hubený

Matula

et al. 2007

Image Processing Based on Partial Differential Equations

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Fast Marching 3D Reconstruction of Interphase Chromosomes

Cited by 5 publications

References 13 publications

Geodesic Methods in Computer Vision and Graphics

Geodesic Methods in Computer Vision and Graphics

Automated acquisition and processing of multidimensional image data in confocal in vivo microscopy

Improved 3D Reconstruction of Interphase Chromosomes Based on Nonlinear Diffusion Filtering

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