New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications

Saito, Toyofumi; Toriwaki, Jun‐ichiro

doi:10.1016/0031-3203(94)90133-3

Cited by 401 publications

(286 citation statements)

References 12 publications

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“…First, parallel algorithms were presented by Yamada [4], Mitchell [16,18], or Embrechts [20], but they cannot be efficiently implemented on a general-purpose computer. Second, raster scanning algorithms were proposed by Mullikin [12] or Saito [19]. Third, propagation or contour-processing algorithms were introduced by Vincent [9], Ragnemalm [14], and Eggers [24].…”

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

confidence: 99%

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Cuisenaire

Macq

1999

Computer Vision and Image Understanding

127

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

confidence: 99%

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Cuisenaire

Macq

1999

Computer Vision and Image Understanding

127

108

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“…One approach to optimize this task is to use the Euclidean Distance Transform (EDT). This transform computes a distance map for the 3D volume, where each voxel value represents the minimum distance from that voxel to the surface [42,43]. Let I(x,y), x ¼1yN and y ¼1yM, a pixel in an image I, where x and y are the Cartesian coordinates of the pixel I(x,y), and Iðx,yÞ ¼ g, g¼0,y,L is an integer value, which represents the intensity of light in that pixel.…”

Section: Surface Mapping and Dilation Processmentioning

confidence: 99%

Color texture analysis based on fractal descriptors

Backes

Casanova

Bruno

2012

Pattern Recognition

156

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“…Since the signed distance transform can be computed in linear time (e.g. using [19]) the total running time of the algorithm is linear in the size of the CT scan. The algorithm is given in Algorithm 1.…”

Section: Require: Binary Voxel Grids For Organ O and Tumormentioning

confidence: 99%

A Shape Relationship Descriptor for Radiation Therapy Planning

Kazhdan

Simari

McNutt

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

102

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Abstract. In this paper we address the challenge of matching patient geometry to facilitate the design of patient treatment plans in radiotherapy. To this end we propose a novel shape descriptor, the Overlap Volume Histogram, which provides a rotation and translation invariant representation of a patient's organs at risk relative to the tumor volume. Using our descriptor, it is possible to accurately identify database patients with similar constellations of organ and tumor geometries, enabling the transfer of treatment plans between patients with similar geometries. We demonstrate the utility of our method for such tasks by outperforming state of the art shape descriptors in the retrieval of patients with similar treatment plans. We also preliminarily show its potential as a quality control tool by demonstrating how it is used to identify an organ at risk whose dose can be significantly reduced.

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New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications

Cited by 401 publications

References 12 publications

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Color texture analysis based on fractal descriptors

A Shape Relationship Descriptor for Radiation Therapy Planning

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