This title appears in the Scientific Report : 2012 
Automatic identification of gray and white matter components in polarized light imaging
Dammers, J.
Breuer, L. / Axer, M. / Kleiner, M. / Eiben, B. / Gräßel, D. / Dickscheid, T. / Zilles, K. / Amunts, K. / Shah, N.J. / Pietrzyk, U.
Strukturelle und funktionelle Organisation des Gehirns; INM-1
Physik der Medizinischen Bildgebung; INM-4
Molekulare Organisation des Gehirns; INM-2
NeuroImage, 59 (2012) 2, S. 1338–1347
Orlando, Fla. Academic Press 2012
1338–1347
10.1016/j.neuroimage.2011.08.030
21875673
Journal Article
Theory, modelling and simulation
Funktion und Dysfunktion des Nervensystems
NeuroImage 59
Algorithms
Artificial Intelligence
Brain: cytology
Humans
Image Enhancement: methods
Image Interpretation, Computer-Assisted: methods
Lighting: methods
Microscopy, Polarization: methods
Nerve Fibers, Myelinated: ultrastructure
Neurons: cytology
Pattern Recognition, Automated: methods
Reproducibility of Results
Sensitivity and Specificity
Please use the identifier: http://dx.doi.org/10.1016/j.neuroimage.2011.08.030 in citations.

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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. 
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 
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