Elsevier

NeuroImage

Volume 59, Issue 2, 16 January 2012, Pages 1338-1347
NeuroImage

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

https://doi.org/10.1016/j.neuroimage.2011.08.030Get rights and content

Abstract

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.

Highlights

► Novel approach to automatically identify gray and white matter components from PLI. ► User independent way of signal enhancement in a large set of PLI images. ► The method includes artifact and noise rejection utilizing ICA. ► Regions with weak intensities are greatly enhanced after ICA. ► Our test statistic is sensitive to both, missing components and changes in SNR.

Introduction

Polarized light imaging (PLI) is a powerful tool to identify nerve fibers and their spatial orientation in microtome sections of human postmortem brains. PLI provides a spatial resolution in the micrometer range. The method utilizes the optical birefringence of the myelin sheaths surrounding axons (Göthlin, 1913, Schmidt, 1923, Schmitt and Bear, 1937). PLI enables the identification and the definition of the spatial orientation of nerve fibers and bundles in 100 μm thick (and thinner) microtome sections of postmortem brains (Axer et al., 2001, Axer et al., 2011, Larsen et al., 2007). We recently introduced three-dimensional polarized light imaging (3D-PLI) and mapping of the three-dimensional courses of fiber tracts in the human brain (Axer et al., 2011, Dammers et al., 2010, Gräßel et al., 2009, Palm et al., 2010).

In PLI, the light intensity is measured by a charged-coupled device (CCD) camera and varies dependent on the rotation angle of the polarimeter system relative to the brain section. The intensity variation is registered as a sinusoidal light intensity profile, where the in-plane (direction) and out-of-section (inclination) fiber orientations are directly related to the captured intensities in terms of peak position and signal amplitude, respectively. The precise measurement of fiber orientation depends on the quality of the measured signal in the microtome section. Its accuracy is influenced by the scatter properties of the investigated object, which in turn depend on the section thickness. Moreover, filter inhomogeneities of the polarizers and possible reflections from the polarimeter gantry may influence the PLI signal. Another possible source of artifacts is dust on the polarizer, although the polarimeter is operated in a shielded construction to prevent for external light and dust particles. As a result of the rotation of the polarizer dust particles will derogate the measured light intensity only once within a half circle, if and only if they are located on the rotating system (crossed polarizers, quarter-wave retarder). Hence, the measured intensity at the CCD array is a linear mixture of different light sources.

Recently, we introduced independent component analysis (ICA) for signal enhancement and restoration in PLI images (Dammers et al., 2010). We showed that ICA is capable of effectively removing the superposition effect of light originating from different sources in polarized light images. As a result, component maps corresponding to gray and white matter structures as well as noise and artifacts can be identified. In a single postmortem brain, however, the number of component maps can be in the range of a few ten thousand depending on the size of the postmortem brain, the slice thickness, and the number of rotation angles used during data acquisition. Here we introduce a fully automated and user independent way of identifying the components of interest; i.e., components that are related to gray and white matter after ICA decomposition.

Section snippets

Image acquisition

The study is based on sections of a human postmortem brain from the body donor program of the University Aachen (RWTH Aachen, Germany) in accordance with legal requirements. After brain removal and fixation in formalin, the brain was cryo-protected, embedded in gelatine, and frozen. Axial sectioning (section thickness 100 μm) was performed on a cryostat microtome (Polycut CM 3500, Leica, Germany). A digital color image of the frozen block (block-face image) was obtained of each section during

Results

PLI signal acquisition was applied to 770 histological sections at 18 different rotation angles using an angle step size of 10°. The range where the histological sections were taken from includes gray and white matter of various anatomical structures and covers about ¾ of the full postmortem brain. The ICA decomposition of the full PLI data set resulted in 13860 independent component (IC) maps.

Discussion

The aim of the present study was to develop an automatic and user independent tool for the separation of gray and white matter components from noise and artifacts in polarized light imaging (PLI) using independent component analysis (ICA). In order to enhance images acquired by PLI, successful applications of ICA have recently been demonstrated in (Axer et al., 2011, Dammers et al., 2010, Gräßel et al., 2009, Palm et al., 2010). In these studies, the ICA filtering effectively removed noise and

Acknowledgments

This work was supported by the Initiative and Networking Fund of the Helmholtz Association within the Helmholtz Alliance on Systems Biology (“Human Brain Model”). Moreover, we like to thank Prof. Dr. Andreas Prescher, MD (Institut für Molekulare und Zelluläre Anatomie, RWTH Aachen University, Germany) for the provision of excellent human brain tissue. We are grateful to Markus Cremer (Institute of Neuroscience and Medicine, INM-2, Research Centre Jülich, Germany) for excellent technical

References (18)

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