000016171 001__ 16171
000016171 005__ 20210129210642.0
000016171 0247_ $$2pmid$$apmid:21875673
000016171 0247_ $$2DOI$$a10.1016/j.neuroimage.2011.08.030
000016171 0247_ $$2WOS$$aWOS:000298210600054
000016171 0247_ $$2altmetric$$aaltmetric:396783
000016171 037__ $$aPreJuSER-16171
000016171 041__ $$aeng
000016171 082__ $$a610
000016171 1001_ $$0P:(DE-Juel1)VDB261$$aDammers, J.$$b0$$uFZJ
000016171 245__ $$aAutomatic identification of gray and white matter components in polarized light imaging
000016171 260__ $$aOrlando, Fla.$$bAcademic Press$$c2012
000016171 300__ $$a1338–1347
000016171 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article
000016171 3367_ $$2DataCite$$aOutput Types/Journal article
000016171 3367_ $$00$$2EndNote$$aJournal Article
000016171 3367_ $$2BibTeX$$aARTICLE
000016171 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000016171 3367_ $$2DRIVER$$aarticle
000016171 440_0 $$04545$$aNeuroImage$$v59$$x1053-8119$$y1338 - 1347
000016171 500__ $$aRecord converted from VDB: 12.11.2012
000016171 520__ $$aPolarized light imaging (PLI) enables the visualization of fiber tracts with high spatial resolution in microtome sections of postmortem brains. Vectors of the fiber orientation defined by inclination and direction angles can directly be derived from the optical signals employed by PLI analysis. The polarization state of light propagating through a rotating polarimeter is varied in such a way that the detected signal of each spatial unit describes a sinusoidal signal. Noise, light scatter and filter inhomogeneities, however, interfere with the original sinusoidal PLI signals, which in turn have direct impact on the accuracy of subsequent fiber tracking. Recently we showed that the primary sinusoidal signals can effectively be restored after noise and artifact rejection utilizing independent component analysis (ICA). In particular, regions with weak intensities are greatly enhanced after ICA based artifact rejection and signal restoration. Here, we propose a user independent way of identifying the components of interest after decomposition; i.e., components that are related to gray and white matter. Depending on the size of the postmortem brain and the section thickness, the number of independent component maps can easily be in the range of a few ten thousand components for one brain. Therefore, we developed an automatic and, more importantly, user independent way of extracting the signal of interest. The automatic identification of gray and white matter components is based on the evaluation of the statistical properties of the so-called feature vectors of each individual component map, which, in the ideal case, shows a sinusoidal waveform. Our method enables large-scale analysis (i.e., the analysis of thousands of whole brain sections) of nerve fiber orientations in the human brain using polarized light imaging.
000016171 536__ $$0G:(DE-Juel1)FUEK409$$2G:(DE-HGF)$$aFunktion und Dysfunktion des Nervensystems (FUEK409)$$cFUEK409$$x0
000016171 536__ $$0G:(DE-HGF)POF2-89574$$a89574 - Theory, modelling and simulation (POF2-89574)$$cPOF2-89574$$fPOF II T$$x1
000016171 588__ $$aDataset connected to Pubmed
000016171 650_2 $$2MeSH$$aAlgorithms
000016171 650_2 $$2MeSH$$aArtificial Intelligence
000016171 650_2 $$2MeSH$$aBrain: cytology
000016171 650_2 $$2MeSH$$aHumans
000016171 650_2 $$2MeSH$$aImage Enhancement: methods
000016171 650_2 $$2MeSH$$aImage Interpretation, Computer-Assisted: methods
000016171 650_2 $$2MeSH$$aLighting: methods
000016171 650_2 $$2MeSH$$aMicroscopy, Polarization: methods
000016171 650_2 $$2MeSH$$aNerve Fibers, Myelinated: ultrastructure
000016171 650_2 $$2MeSH$$aNeurons: cytology
000016171 650_2 $$2MeSH$$aPattern Recognition, Automated: methods
000016171 650_2 $$2MeSH$$aReproducibility of Results
000016171 650_2 $$2MeSH$$aSensitivity and Specificity
000016171 7001_ $$0P:(DE-Juel1)131637$$aBreuer, L.$$b1$$uFZJ
000016171 7001_ $$0P:(DE-Juel1)VDB67318$$aAxer, M.$$b2$$uFZJ
000016171 7001_ $$0P:(DE-Juel1)VDB100171$$aKleiner, M.$$b3$$uFZJ
000016171 7001_ $$0P:(DE-Juel1)VDB62239$$aEiben, B.$$b4$$uFZJ
000016171 7001_ $$0P:(DE-Juel1)131642$$aGräßel, D.$$b5$$uFZJ
000016171 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, T.$$b6$$uFZJ
000016171 7001_ $$0P:(DE-Juel1)131714$$aZilles, K.$$b7$$uFZJ
000016171 7001_ $$0P:(DE-Juel1)131631$$aAmunts, K.$$b8$$uFZJ
000016171 7001_ $$0P:(DE-Juel1)131794$$aShah, N.J.$$b9$$uFZJ
000016171 7001_ $$0P:(DE-Juel1)VDB2211$$aPietrzyk, U.$$b10$$uFZJ
000016171 773__ $$0PERI:(DE-600)1471418-8$$a10.1016/j.neuroimage.2011.08.030$$gVol. 59$$n2$$p1338–1347$$q59$$tNeuroImage$$v59$$x1053-8119$$y2012
000016171 8567_ $$uhttp://dx.doi.org/10.1016/j.neuroimage.2011.08.030
000016171 909CO $$ooai:juser.fz-juelich.de:16171$$pVDB
000016171 915__ $$0StatID:(DE-HGF)0010$$2StatID$$aJCR/ISI refereed
000016171 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR
000016171 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000016171 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000016171 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000016171 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000016171 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000016171 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000016171 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000016171 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz
000016171 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences
000016171 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000016171 9141_ $$y2012
000016171 9132_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000016171 9131_ $$0G:(DE-HGF)POF2-89574$$1G:(DE-HGF)POF3-890$$2G:(DE-HGF)POF3-800$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vTheory, modelling and simulation$$x1
000016171 9201_ $$0I:(DE-Juel1)INM-1-20090406$$gINM$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x1
000016171 9201_ $$0I:(DE-Juel1)INM-2-20090406$$gINM$$kINM-2$$lMolekulare Organisation des Gehirns$$x0
000016171 9201_ $$0I:(DE-Juel1)INM-4-20090406$$gINM$$kINM-4$$lPhysik der Medizinischen Bildgebung$$x2
000016171 970__ $$aVDB:(DE-Juel1)129901
000016171 980__ $$aVDB
000016171 980__ $$aConvertedRecord
000016171 980__ $$ajournal
000016171 980__ $$aI:(DE-Juel1)INM-1-20090406
000016171 980__ $$aI:(DE-Juel1)INM-2-20090406
000016171 980__ $$aI:(DE-Juel1)INM-4-20090406
000016171 980__ $$aUNRESTRICTED
000016171 981__ $$aI:(DE-Juel1)INM-2-20090406
000016171 981__ $$aI:(DE-Juel1)INM-4-20090406