Description: Automatic identification of gray and white matter components in polarized light imaging

This title appears in the Scientific Report : 2012

Automatic identification of gray and white matter components in polarized light imaging

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...

Personal Name(s):	Dammers, J.
	Breuer, L. / Axer, M. / Kleiner, M. / Eiben, B. / Gräßel, D. / Dickscheid, T. / Zilles, K. / Amunts, K. / Shah, N.J. / Pietrzyk, U.
Contributing Institute:	Strukturelle und funktionelle Organisation des Gehirns; INM-1 Physik der Medizinischen Bildgebung; INM-4 Molekulare Organisation des Gehirns; INM-2
Published in:	NeuroImage, 59 (2012) 2, S. 1338–1347
Imprint:	Orlando, Fla. Academic Press 2012
Physical Description:	1338–1347
DOI:	10.1016/j.neuroimage.2011.08.030
PubMed ID:	21875673
Document Type:	Journal Article
Research Program:	Theory, modelling and simulation Funktion und Dysfunktion des Nervensystems
Series Title:	NeuroImage 59
Subject (ZB):	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
	Publikationsportal JuSER

Please use the identifier: http://dx.doi.org/10.1016/j.neuroimage.2011.08.030 in citations.

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.