Improving the visualization of electron-microscopy data through optical flow interpolation

Handle URI:
http://hdl.handle.net/10754/564658
Title:
Improving the visualization of electron-microscopy data through optical flow interpolation
Authors:
Carata, Lucian; Shao, Dan; Hadwiger, Markus ( 0000-0003-1239-4871 ) ; Gröeller, Eduard
Abstract:
Technical developments in neurobiology have reached a point where the acquisition of high resolution images representing individual neurons and synapses becomes possible. For this, the brain tissue samples are sliced using a diamond knife and imaged with electron-microscopy (EM). However, the technique achieves a low resolution in the cutting direction, due to limitations of the mechanical process, making a direct visualization of a dataset difficult. We aim to increase the depth resolution of the volume by adding new image slices interpolated from the existing ones, without requiring modifications to the EM image-capturing method. As classical interpolation methods do not provide satisfactory results on this type of data, the current paper proposes a re-framing of the problem in terms of motion volumes, considering the depth axis as a temporal axis. An optical flow method is adapted to estimate the motion vectors of pixels in the EM images, and this information is used to compute and insert multiple new images at certain depths in the volume. We evaluate the visualization results in comparison with interpolation methods currently used on EM data, transforming the highly anisotropic original dataset into a dataset with a larger depth resolution. The interpolation based on optical flow better reveals neurite structures with realistic undistorted shapes, and helps to easier map neuronal connections. © 2011 ACM.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Visual Computing Center (VCC)
Publisher:
Association for Computing Machinery (ACM)
Journal:
Proceedings of the 27th Spring Conference on Computer Graphics - SCCG '11
Conference/Event name:
27th Spring Conference on Computer Graphics, SCCG 2011
Issue Date:
2013
DOI:
10.1145/2461217.2461238
Type:
Conference Paper
ISBN:
9781450319782
Appears in Collections:
Conference Papers; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorCarata, Lucianen
dc.contributor.authorShao, Danen
dc.contributor.authorHadwiger, Markusen
dc.contributor.authorGröeller, Eduarden
dc.date.accessioned2015-08-04T07:11:13Zen
dc.date.available2015-08-04T07:11:13Zen
dc.date.issued2013en
dc.identifier.isbn9781450319782en
dc.identifier.doi10.1145/2461217.2461238en
dc.identifier.urihttp://hdl.handle.net/10754/564658en
dc.description.abstractTechnical developments in neurobiology have reached a point where the acquisition of high resolution images representing individual neurons and synapses becomes possible. For this, the brain tissue samples are sliced using a diamond knife and imaged with electron-microscopy (EM). However, the technique achieves a low resolution in the cutting direction, due to limitations of the mechanical process, making a direct visualization of a dataset difficult. We aim to increase the depth resolution of the volume by adding new image slices interpolated from the existing ones, without requiring modifications to the EM image-capturing method. As classical interpolation methods do not provide satisfactory results on this type of data, the current paper proposes a re-framing of the problem in terms of motion volumes, considering the depth axis as a temporal axis. An optical flow method is adapted to estimate the motion vectors of pixels in the EM images, and this information is used to compute and insert multiple new images at certain depths in the volume. We evaluate the visualization results in comparison with interpolation methods currently used on EM data, transforming the highly anisotropic original dataset into a dataset with a larger depth resolution. The interpolation based on optical flow better reveals neurite structures with realistic undistorted shapes, and helps to easier map neuronal connections. © 2011 ACM.en
dc.publisherAssociation for Computing Machinery (ACM)en
dc.subjectinterpolationen
dc.subjectoptical flowen
dc.subjectvolume visualizationen
dc.titleImproving the visualization of electron-microscopy data through optical flow interpolationen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.identifier.journalProceedings of the 27th Spring Conference on Computer Graphics - SCCG '11en
dc.conference.date28 April 2011 through 30 April 2011en
dc.conference.name27th Spring Conference on Computer Graphics, SCCG 2011en
dc.conference.locationVinicneen
dc.contributor.institutionFaculty of Automatic Control and Computer Engineering, Gh. Asachi Technical University of Iasi, Romaniaen
dc.contributor.institutionInstitute of Computer Graphics and Algorithms, Vienna University of Technology, Austriaen
kaust.authorHadwiger, Markusen
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