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dc.contributor.author
Gebäck, Tobias
dc.contributor.author
Koumoutsakos, Petros
dc.date.accessioned
2018-09-04T12:21:27Z
dc.date.available
2017-06-14T11:47:02Z
dc.date.available
2018-09-04T12:21:27Z
dc.date.issued
2009-03
dc.identifier.issn
1471-2105
dc.identifier.other
10.1186/1471-2105-10-75
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/157117
dc.identifier.doi
10.3929/ethz-b-000157117
dc.description.abstract
Background Despite significant progress in imaging technologies, the efficient detection of edges and elongated features in images of intracellular and multicellular structures acquired using light or electron microscopy is a challenging and time consuming task in many laboratories. Results We present a novel method, based on the discrete curvelet transform, to extract a directional field from the image that indicates the location and direction of the edges. This directional field is then processed using the non-maximal suppression and thresholding steps of the Canny algorithm to trace along the edges and mark them. Optionally, the edges may then be extended along the directions given by the curvelets to provide a more connected edge map. We compare our scheme to the Canny edge detector and an edge detector based on Gabor filters, and show that our scheme performs better in detecting larger, elongated structures possibly composed of several step or ridge edges. Conclusion The proposed curvelet based edge detection is a novel and competitive approach for imaging problems. We expect that the methodology and the accompanying software will facilitate and improve edge detection in images available using light or electron microscopy.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/2.0/
dc.subject
Directional Field
en_US
dc.subject
Gabor Filter
en_US
dc.subject
Coarse Level
en_US
dc.subject
Step Edge
en_US
dc.subject
Canny Edge Detector
en_US
dc.title
Edge detection in microscopy images using curvelets
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
ethz.journal.title
BMC Bioinformatics
ethz.journal.volume
10
en_US
ethz.pages.start
75
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
004240301
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::03499 - Koumoutsakos, Petros / Koumoutsakos, Petros
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::03499 - Koumoutsakos, Petros / Koumoutsakos, Petros
ethz.date.deposited
2017-06-14T11:54:39Z
ethz.source
ECIT
ethz.identifier.importid
imp59364ce14f72216354
ethz.ecitpid
pub:35403
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-13T08:47:43Z
ethz.rosetta.lastUpdated
2020-02-15T14:44:05Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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