Weighted distance transforms for volume images digitized in elongated voxel grids

Sintorn, Ida‐Maria; Borgefors, Gunilla

doi:10.1016/j.patrec.2003.12.006

Cited by 15 publications

(11 citation statements)

References 12 publications

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“…A further study of this effect could lead to a more adaptable and faster search strategy for 3D-FEED. Further, the FEED principle can also be adapted to images with "non-square" voxels, a kind of image often used in medical application and produced by tomographic devices or by confocal microscopes 13 .…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional fast exact Euclidean distance (3D-FEED) maps

Schouten

Kuppens

Broek

2006

Vision Geometry XIV

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In image and video analysis, distance maps are frequently used. They provide the (Euclidean) distance (ED) of background pixels to the nearest object pixel. Recently, the Fast Exact Euclidean Distance (FEED) transformation was launched. In this paper, we present the three dimensional (3D) version of FEED. 3D-FEED is compared with four other methods for a wide range of 3D test images. 3D-FEED proved to be twice as fast as the fastest algorithm available. Moreover, it provides true exact EDs, where other algorithms only approximate the ED. This unique algorithm makes the difference, especially there where time and precision are of importance.

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Section: Discussionmentioning

confidence: 99%

Three-dimensional fast exact Euclidean distance (3D-FEED) maps

Schouten

Kuppens

Broek

2006

Vision Geometry XIV

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“…These calculation have been extended to larger masks [23,24] and to higher dimensions [7]. Anisotropic lattices have also been considered [8,25,26]. However, those calculations remain tedious, are not systematized and thus have to be conducted manually for every mask size or anisotropy value.…”

Section: Optimal Coefficients Calculationmentioning

confidence: 99%

“…While modern computer disk capacity is sufficient to store typically a few anisotropic mosaics together with the associated results, they cannot accommodate as many in the isotropic case (a typical acquisition consists in a mosaic of more than 100 images, covering an area between 25 and 100 mm 2 ). The computation of optimal chamfer coefficients for anisotropic images has also been discussed in the literature [8], but here again in a non-automated fashion. Inevitable variations of the anisotropy factor (the ratio between the inter-slice distance and the pixel size) between acquisitions prompted us to develop a fully automated algorithm to compute optimal chamfer coefficients in anisotropic lattices.…”

Section: Introductionmentioning

confidence: 99%

3-D chamfer distances and norms in anisotropic grids

Fouard

Malandain

2005

Image and Vision Computing

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Chamfer distances are widely used in image analysis and many authors have investigated the computation of optimal chamfer mask coefficients. Unfortunately, these methods are not systematized: calculations have to be conducted manually for every mask size or image anisotropy. Since image acquisition (e.g. medical imaging) can lead to discrete anisotropic grids with unpredictable anisotropy value, automated calculation of chamfer mask coefficients becomes mandatory for efficient distance map computations. This article presents an automatic construction for chamfer masks of arbitrary sizes. This allows, first, to derive analytically the relative error with respect to the Euclidean distance, in any 3-D anisotropic lattice, and second, to compute optimal chamfer coefficients. In addition, the resulting chamfer map verifies discrete norm conditions. q 2004 Published by Elsevier B.V.

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“…This process has been widely studied and developed on regular grids (see Fabbri et al, 2008 for a survey). Some specific extensions of the DT to nonregular grids also exist, such as elongated grids (Chehadeh et al, 1996;Fouard and Malandain, 2005;Sintorn and Borgefors, 2004), quadtrees/octrees (Samet, 1990;Vörös, 2001), Face-Centered Cubic (FCC)/Body-Centered Cubic (BCC) grids (Fouard et al, 2007), etc. But, to our knowledge, no generic DT process has been designed on every kinds of image representations in two or higher dimensions.…”

Section: Introductionmentioning

confidence: 99%