Anti-Aliased Euclidean Distance Transform on 3D Sampling Lattices
2014 (English)In: Discrete Geometry for Computer Imagery: 18th IAPR International Conference, DGCI 2014, Siena, Italy, September 10-12, 2014. Proceedings / [ed] Elena Barcucci, Andrea Frosini, Simone Rinaldi, 2014, 88-98 p.Conference paper (Refereed)
The Euclidean distance transform (EDT) is used in many essential operations in image processing, such as basic morphology, level sets, registration and path finding. The anti-aliased Euclidean distance transform (AAEDT), previously presented for two-dimensional images, uses the gray-level information in, for example, area sampled images to calculate distances with sub-pixel precision. Here, we extend the studies of AAEDT to three dimensions, and to the Body-Centered Cubic (BCC) and Face-Centered Cubic (FCC) lattices, which are, in many respects, considered the optimal three-dimensional sampling lattices. We compare different ways of converting gray-level information to distance values, and find that the lesser directional dependencies of optimal sampling lattices lead to better approximations of the true Euclidean distance.
Place, publisher, year, edition, pages
2014. 88-98 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 8668
Medical Image Processing
Research subject Computerized Image Processing
IdentifiersURN: urn:nbn:se:uu:diva-237982DOI: 10.1007/978-3-319-09955-2_8ISI: 000358195100008ISBN: 978-3-319-09954-5ISBN: 978-3-319-09955-2OAI: oai:DiVA.org:uu-237982DiVA: diva2:769620
Discrete Geometry for Computer Imagery, 18th IAPR International Conference, DGCI 2014, Siena, Italy, September 10-12, 2014