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

Schouten, Theo E.; Kuppens, Harco; Broek, Egon L. van den

doi:10.1117/12.643721

Cited by 10 publications

(8 citation statements)

References 16 publications

(14 reference statements)

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“…Iǧdem et al, 2010). Although not discussed in this article, FEED can also be applied on 3D images (Schouten et al, 2006) and video (Schouten and van den Broek, 2010).…”

Section: Discussionmentioning

confidence: 99%

Modeling Internal Radiation Therapy

Broek

Schouten

2011

Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms

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Abstract:A new technique is described to model (internal) radiation therapy. It is founded on morphological processing, in particular distance transforms. Its formal basis is presented as well as its implementation via the Fast Exact Euclidean Distance (FEED) transform. Its use for all variations of internal radiation therapy is described. In a benchmark trial, FEED proved to be truly exact as well as faster than a comparable technique. These features can be of crucial importance in radiation therapy as the balance between maximization of treatment effect and doses that cause unwanted damage to healthy tissue is fragile. This balance can be secured using the modeling technique presented here.

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“…Iǧdem et al, 2010). Although not discussed in this article, FEED can also be applied on 3D images (Schouten et al, 2006) and video (Schouten and van den Broek, 2010).…”

Section: Discussionmentioning

confidence: 99%

Modeling Internal Radiation Therapy

Broek

Schouten

2011

Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms

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“…However, most often they are applied in 2D or 3D [32,[35][36][37]. The reason for this is simple; most digital images are 2D, some are 3D, as it is well illustrated by the areas of application just mentioned.…”

Section: Current and Future Developmentsmentioning

confidence: 99%

“…2), in practice often a two-dimensional (2D) or three-dimensional (3D) space is required [32,[35][36][37], as most digital images are 2D or 3D. For higher dimensional spaces (i.e., n > 3), applications are less apparent and, hence, little patent applications will be filed in this area.…”

Section: Distance Transformmentioning

confidence: 99%

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Distance Transforms: Academics Versus Industry

Broek

Schouten²

2011

CSENG

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Abstract:In image and video analysis, distance transformations (DT) are frequently used. They provide a distance image (DI) of background pixels to the nearest object pixel. DT touches upon the core of many applications; consequently, not only science but also industry has conducted a significant body of work in this field. However, in a vast majority of the cases this has not been published in major scientific outlets but has been filed as a patent application. This article provides a brief introduction into DT, including a specification of a few of the most prominent algorithms in the field. Next, a few interesting algorithms from the last decade are discussed. A benchmark including eight DT algorithms (i.e., city block, Danielsson's algorithm, chamfer 3-4, hexadecagonal region growing, a recent claimed true Euclidean DT, and three exact Euclidean DT) has been executed, which illustrates the intriguing complexity of DT in terms of precision and computational complexity. Subsequently, a selection of key patent applications are discussed that have emerged in this field, including their scientific merit and areas of application. Finally, this article's findings are summarized and discussed, with an emphasis on both the common ground of scientific articles and patent applications as well as the added value they can have to each other.

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“…Note that for all x ∈ X, the value D X (x) is the radius of the maximum ball centered in x that is completely contained in X. D X can be computed in linear time using any of the algorithms presented in Meijster et al (2002), Maurer et al (2003) or Schouten et al (2006).…”

Section: Notions For Digital Imagesmentioning

confidence: 99%

Statistical Analysis of the Local Strut Thickness of Open Cell Foams

Liebscher¹,

Redenbach²

2013

Image Anal Stereol

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Open cell foams are formed by an interconnected network of struts whose thickness varies locally. These variations are known to have an impact on the elastic and thermal properties of the foam. In this paper we quantify the local strut thickness by means of micro computed tomography (µCT) imaging. We develop a fully automatic algorithm to extract the foam's skeleton from a binary image and its topological decomposition into vertices and struts. This allows to estimate the thickness of individual strut segments by the Euclidean distance transform, where an appropriate correction for struts with nonspherical cross-sectional shape is applied. Conflating these estimates based on the strut lengths results in a strut thickness profile for the entire foam. Based on this profile we give a statistical justification that a strut thickness model should depend on the strut length. Furthermore, the investigation of polynomial models for the strut thickness profile by means of regression analysis leads to a new three-parameter strut thickness model.

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

Cited by 10 publications

References 16 publications

Modeling Internal Radiation Therapy

Modeling Internal Radiation Therapy

Distance Transforms: Academics Versus Industry

Statistical Analysis of the Local Strut Thickness of Open Cell Foams

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