Fast raster scan distance propagation on the discrete rectangular lattice

Leymarie, Frederic Fol; Levine, Martin D.

doi:10.1016/1049-9660(92)90008-q

Cited by 88 publications

(52 citation statements)

References 11 publications

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“…In the first case, all boundary end points with an empirically determined maximum distance of d = 20 pixels are linked to close gaps in between. In the second case, we apply a Euclidean distance transformation to the binary image, followed by a watershed transformation on the distance image using the implementation available in ImageJ according to Leymarie and Levine (1992). To remove implausible boundaries resulting from oversegmentation of the watershed segmentation, we apply a combination of different criteria for filtering boundary segments.…”

Section: Cell Boundary Segmentation and Region Filteringmentioning

confidence: 99%

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

et al. 2017

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Section: Cell Boundary Segmentation and Region Filteringmentioning

confidence: 99%

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

et al. 2017

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“…Borgefors proposes a chamfer DT using two raster scans, but only provides a much coarser approximation of the Euclidean metric. Leymarie [13] showed that, if implemented carefully, both approximations have similar computational cost. Ragnemalm [14] proposed an ordered propagation version of Danielsson's algorithm, as well as a raster scan implementation [17] using a minimal number of scans.…”

Section: D( P) = Min{dist( P Q) Q ∈ O}mentioning

confidence: 99%

“…In other words, belonging to a given Voronoi tile is not a local property: the tile to which a pixel belongs cannot always be deduced from the tiles to which its neighbors belong. Because they propagate the information locally from neighbor to neighbor, both raster scanning and propagation DT algorithms [2,13,14,17] will provide a wrong value for D(q) at Fig. 1.…”

Section: Voronoi Diagrams and Distance Transformationsmentioning

confidence: 99%

“…Leymarie [13] recommends dist E (dp + n) = dist E (dp) + 2 · dp x + 1 if n = (1, 0), dist E (dp + n) = dist E (dp) + 2 · dp y + 1 if n = (0, 1), etc. which only requires using additions, one shift and one increment.…”

Section: Iterationsmentioning

confidence: 99%

“…For n × n images, simple approximate algorithms such as Danielsson [2], chamfer DT [3] or Leymarie [13] have a fixed o(n 2 ) cost, regardless of the image content. Yamada's parallel algorithm [4] has a o(d · n 2 ) cost proportional to the maximum distance d found in the image.…”

Section: Complexity and Computational Costmentioning

confidence: 99%

See 2 more Smart Citations

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Cuisenaire

Macq

1999

Computer Vision and Image Understanding

124

108

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We propose a new exact Euclidean distance transformation (DT) by propagation, using bucket sorting. A fast but approximate DT is first computed using a coarse neighborhood. A sequence of larger neighborhoods is then used to gradually improve this approximation. Computations are kept short by restricting the use of these large neighborhoods to the tile borders in the Voronoi diagram of the image. We assess the computational cost of this new algorithm and show that it is both smaller and less image-dependent than all other DTs recently proposed. Contrary to all other propagation DTs, it appears to remain o(n 2 ) even in the worst-case scenario.

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Getting the whole picture: High content screening using three‐dimensional cellular model systems and whole animal assays

Kriston‐Vizi

Flotow

2016

Cytometry Pt A

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Phenotypic or High Content Screening (HCS) is becoming more widely used for primary screening campaigns in drug discovery. Currently the vast majority of HCS campaigns are using cell lines grown in well-established monolayer cultures (2D tissue culture). There is widespread recognition that the more biologically relevant 3D tissue culture technologies such as spheroids and organoids and even whole animal assays will eventually be run as primary HCS. Upgrading the IT infrastructure to cope with the increase in data volumes requires investments in hardware (and software) and this will be manageable. However, the main bottleneck for the effective adoption and use of 3D tissue culture and whole animal assays in HCS is anticipated to be the development of software for the analysis of 3D images. In this review we summarize the current state of the available software and how they may be applied to analyzing 3D images obtained from a HCS campaign. © 2016 International Society for Advancement of Cytometry.

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Fast raster scan distance propagation on the discrete rectangular lattice

Cited by 88 publications

References 11 publications

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Getting the whole picture: High content screening using three‐dimensional cellular model systems and whole animal assays

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