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dc.contributor.authorDietenbeck, Thomas
dc.contributor.authorVarray, François
dc.contributor.authorKybic, Jan
dc.contributor.authorBasset, Olivier
dc.contributor.authorCachard, Christian
dc.date.accessioned2014-12-05T15:46:54Z
dc.date.available2014-12-05T15:46:54Z
dc.date.issued2014
dc.identifier.citationDIETENBECK, T. - VARRAY, F. - KYBIC, J. - BASSET, O. - CACHARD, C.: Neuromuscular fiber segmentation through particle filtering and discrete optimization. In SPIE Medical Imaging. Bellingham: SPIE, 2014, vol. 9034, p. 90340B. ISSN 0277-786X. ISBN 978-0-8194-9827-4. DOI: 10.1117/12.2043257eng
dc.identifier.citationThomas Dietenbeck ; François Varray ; Jan Kybic ; Olivier Basset and Christian Cachard " Neuromuscular fiber segmentation through particle filtering and discrete optimization ", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340B (March 21, 2014); doi:10.1117/12.2043257; http://dx.doi.org/10.1117/12.2043257eng
dc.identifier.isbn978-0-8194-9827-4
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/10467/60947
dc.description.abstractWe present an algorithm to segment a set of parallel, intertwined and bifurcating bers from 3D images, targeted for the identi cation of neuronal bers in very large sets of 3D confocal microscopy images. The method consists of preprocessing, local calculation of ber probabilities, seed detection, tracking by particle ltering, global supervised seed clustering and nal voxel segmentation. The preprocessing uses a novel random local probability ltering (RLPF). The ber probabilities computation is performed by means of SVM using steerable lters and the RLPF outputs as features. The global segmentation is solved by discrete optimization. The combination of global and local approaches makes the segmentation robust, yet the individual data blocks can be processed sequentially, limiting memory consumption. The method is automatic but e cient manual interactions are possible if needed. The method is validated on the Neuromuscular Projection Fibers dataset from the Diadem Challenge. On the 15 rst blocks presented, our method has a 99.4% detection rate. We also compare our segmentation results to a state-of-the-art method. On average, the performances of our method are either higher or equivalent to that of the state-of-the-art method but less user interactions are needed in our approach.eng
dc.language.isoencze
dc.publisherSPIEeng
dc.relation.urihttp://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1852425
dc.rightsCopyright 2014 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.eng
dc.subjectsegmentationeng
dc.subjectconfocal microscopyeng
dc.subjectneuromuscular fibereng
dc.subjectneuron tracingeng
dc.subjectDiadem Challengeeng
dc.subjectSVMeng
dc.subjectparticle filteringeng
dc.subjectN-cuteng
dc.titleNeuromuscular fiber segmentation through particle filtering and discrete optimizationeng
dc.typeArticleeng
dc.identifier.doihttp://dx.doi.org/10.1117/12.2043257


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