Gast ::
| Hauptseite > Publikationsdatenbank > Automatic identification of gray and white matter components in polarized light imaging > print |
| Hauptseite > Publikationsdatenbank > Automatic identification of gray and white matter components in polarized light imaging > print |
| 001 | 16171 | ||
| 005 | 20210129210642.0 | ||
| 024 | 7 | _ | |2 pmid |a pmid:21875673 |
| 024 | 7 | _ | |2 DOI |a 10.1016/j.neuroimage.2011.08.030 |
| 024 | 7 | _ | |2 WOS |a WOS:000298210600054 |
| 024 | 7 | _ | |2 altmetric |a altmetric:396783 |
| 037 | _ | _ | |a PreJuSER-16171 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |0 P:(DE-Juel1)VDB261 |a Dammers, J. |b 0 |u FZJ |
| 245 | _ | _ | |a Automatic identification of gray and white matter components in polarized light imaging |
| 260 | _ | _ | |a Orlando, Fla. |b Academic Press |c 2012 |
| 300 | _ | _ | |a 1338–1347 |
| 336 | 7 | _ | |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |a Journal Article |
| 336 | 7 | _ | |2 DataCite |a Output Types/Journal article |
| 336 | 7 | _ | |0 0 |2 EndNote |a Journal Article |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 ORCID |a JOURNAL_ARTICLE |
| 336 | 7 | _ | |2 DRIVER |a article |
| 440 | _ | 0 | |0 4545 |a NeuroImage |v 59 |x 1053-8119 |y 1338 - 1347 |
| 500 | _ | _ | |a Record converted from VDB: 12.11.2012 |
| 520 | _ | _ | |a Polarized 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. |
| 536 | _ | _ | |0 G:(DE-Juel1)FUEK409 |2 G:(DE-HGF) |x 0 |c FUEK409 |a Funktion und Dysfunktion des Nervensystems (FUEK409) |
| 536 | _ | _ | |0 G:(DE-HGF)POF2-89574 |a 89574 - Theory, modelling and simulation (POF2-89574) |c POF2-89574 |f POF II T |x 1 |
| 588 | _ | _ | |a Dataset connected to Pubmed |
| 650 | _ | 2 | |2 MeSH |a Algorithms |
| 650 | _ | 2 | |2 MeSH |a Artificial Intelligence |
| 650 | _ | 2 | |2 MeSH |a Brain: cytology |
| 650 | _ | 2 | |2 MeSH |a Humans |
| 650 | _ | 2 | |2 MeSH |a Image Enhancement: methods |
| 650 | _ | 2 | |2 MeSH |a Image Interpretation, Computer-Assisted: methods |
| 650 | _ | 2 | |2 MeSH |a Lighting: methods |
| 650 | _ | 2 | |2 MeSH |a Microscopy, Polarization: methods |
| 650 | _ | 2 | |2 MeSH |a Nerve Fibers, Myelinated: ultrastructure |
| 650 | _ | 2 | |2 MeSH |a Neurons: cytology |
| 650 | _ | 2 | |2 MeSH |a Pattern Recognition, Automated: methods |
| 650 | _ | 2 | |2 MeSH |a Reproducibility of Results |
| 650 | _ | 2 | |2 MeSH |a Sensitivity and Specificity |
| 700 | 1 | _ | |0 P:(DE-Juel1)131637 |a Breuer, L. |b 1 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-Juel1)VDB67318 |a Axer, M. |b 2 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-Juel1)VDB100171 |a Kleiner, M. |b 3 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-Juel1)VDB62239 |a Eiben, B. |b 4 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-Juel1)131642 |a Gräßel, D. |b 5 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-Juel1)165746 |a Dickscheid, T. |b 6 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-Juel1)131714 |a Zilles, K. |b 7 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-Juel1)131631 |a Amunts, K. |b 8 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-Juel1)131794 |a Shah, N.J. |b 9 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-Juel1)VDB2211 |a Pietrzyk, U. |b 10 |u FZJ |
| 773 | _ | _ | |0 PERI:(DE-600)1471418-8 |a 10.1016/j.neuroimage.2011.08.030 |g Vol. 59 |n 2 |p 1338–1347 |q 59 |t NeuroImage |v 59 |x 1053-8119 |y 2012 |
| 856 | 7 | _ | |u http://dx.doi.org/10.1016/j.neuroimage.2011.08.030 |
| 909 | C | O | |o oai:juser.fz-juelich.de:16171 |p VDB |
| 913 | 2 | _ | |0 G:(DE-HGF)POF3-574 |1 G:(DE-HGF)POF3-570 |2 G:(DE-HGF)POF3-500 |a DE-HGF |b Key Technologies |l Decoding the Human Brain |v Theory, modelling and simulation |x 0 |
| 913 | 1 | _ | |0 G:(DE-HGF)POF2-89574 |a DE-HGF |v Theory, modelling and simulation |x 1 |4 G:(DE-HGF)POF |1 G:(DE-HGF)POF3-890 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-800 |b Programmungebundene Forschung |l ohne Programm |
| 914 | 1 | _ | |y 2012 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0010 |2 StatID |a JCR/ISI refereed |
| 915 | _ | _ | |0 StatID:(DE-HGF)0100 |2 StatID |a JCR |
| 915 | _ | _ | |0 StatID:(DE-HGF)0110 |2 StatID |a WoS |b Science Citation Index |
| 915 | _ | _ | |0 StatID:(DE-HGF)0111 |2 StatID |a WoS |b Science Citation Index Expanded |
| 915 | _ | _ | |0 StatID:(DE-HGF)0150 |2 StatID |a DBCoverage |b Web of Science Core Collection |
| 915 | _ | _ | |0 StatID:(DE-HGF)0199 |2 StatID |a DBCoverage |b Thomson Reuters Master Journal List |
| 915 | _ | _ | |0 StatID:(DE-HGF)0200 |2 StatID |a DBCoverage |b SCOPUS |
| 915 | _ | _ | |0 StatID:(DE-HGF)0300 |2 StatID |a DBCoverage |b Medline |
| 915 | _ | _ | |0 StatID:(DE-HGF)0310 |2 StatID |a DBCoverage |b NCBI Molecular Biology Database |
| 915 | _ | _ | |0 StatID:(DE-HGF)0420 |2 StatID |a Nationallizenz |
| 915 | _ | _ | |0 StatID:(DE-HGF)1030 |2 StatID |a DBCoverage |b Current Contents - Life Sciences |
| 915 | _ | _ | |0 StatID:(DE-HGF)1050 |2 StatID |a DBCoverage |b BIOSIS Previews |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-1-20090406 |g INM |k INM-1 |l Strukturelle und funktionelle Organisation des Gehirns |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-2-20090406 |g INM |k INM-2 |l Molekulare Organisation des Gehirns |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-4-20090406 |g INM |k INM-4 |l Physik der Medizinischen Bildgebung |x 2 |
| 970 | _ | _ | |a VDB:(DE-Juel1)129901 |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a ConvertedRecord |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a I:(DE-Juel1)INM-1-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-2-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-4-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)INM-2-20090406 |
| 981 | _ | _ | |a I:(DE-Juel1)INM-4-20090406 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|