This title appears in the Scientific Report :
2010
Please use the identifier:
http://dx.doi.org/10.1016/j.neuroimage.2009.08.059 in citations.
Signal enhancement in polarized light imaging by means of independent component analysis
Signal enhancement in polarized light imaging by means of independent component analysis
Polarized light imaging (PLI) enables the evaluation of fiber orientations in histological sections of human postmortem brains, with ultra-high spatial resolution. PLI is based on the birefringent properties of the myelin sheath of nerve fibers. As a result, the polarization state of light propagati...
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Personal Name(s): | Dammers, J. |
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Axer, M. / Gräßel, D. / Palm, C. / Zilles, K. / Amunts, K. / Pietrzyk, U. | |
Contributing Institute: |
Strukturelle und funktionelle Organisation des Gehirns; INM-1 JARA-BRAIN; JARA-BRAIN Molekulare Organisation des Gehirns; INM-2 |
Published in: | NeuroImage, 49 (2010) S. 1241 - 1248 |
Imprint: |
Orlando, Fla.
Academic Press
2010
|
Physical Description: |
1241 - 1248 |
PubMed ID: |
19733674 |
DOI: |
10.1016/j.neuroimage.2009.08.059 |
Document Type: |
Journal Article |
Research Program: |
Theory, modelling and simulation Funktion und Dysfunktion des Nervensystems |
Series Title: |
NeuroImage
49 |
Subject (ZB): | |
Publikationsportal JuSER |
Polarized light imaging (PLI) enables the evaluation of fiber orientations in histological sections of human postmortem brains, with ultra-high spatial resolution. PLI is based on the birefringent properties of the myelin sheath of nerve fibers. As a result, the polarization state of light propagating through a rotating polarimeter is changed in such a way that the detected signal at each measurement unit of a charged-coupled device (CCD) camera describes a sinusoidal signal. Vectors of the fiber orientation defined by inclination and direction angles can then directly be derived from the optical signals employing PLI analysis. However, noise, light scatter and filter inhomogeneities interfere with the original sinusoidal PLI signals. We here introduce a novel method using independent component analysis (ICA) to decompose the PLI images into statistically independent component maps. After decomposition, gray and white matter structures can clearly be distinguished from noise and other artifacts. The signal enhancement after artifact rejection is quantitatively evaluated in 134 histological whole brain sections. Thus, the primary sinusoidal signals from polarized light imaging can be effectively restored after noise and artifact rejection utilizing ICA. Our method therefore contributes to the analysis of nerve fiber orientation in the human brain within a micrometer scale. |