Independent Component Analysis for noise and artifact removal in Three-dimensional Polarized Light Imaging

TL;DR

This paper introduces an automatic ICA-based denoising method for 3D-PLI images, extending its application from mesoscale to microscopic images with efficient computation, demonstrated on primate brain sections.

Contribution

The paper presents a novel automatic denoising procedure using ICA for microscopic 3D-PLI images, enabling noise removal in gray matter regions with reduced computational effort.

Findings

Effective noise removal in microscopic 3D-PLI images

Application to rat and vervet monkey brain sections

Automatic segmentation of gray matter regions

Abstract

In recent years, Independent Component Analysis (ICA) has successfully been applied to remove noise and artifacts in images obtained from Three-dimensional Polarized Light Imaging (3D-PLI) at the mesoscale (i.e., 64 $\mu$m). Here, we present an automatic denoising procedure for gray matter regions that allows to apply the ICA also to microscopic images, with reasonable computational effort. Apart from an automatic segmentation of gray matter regions, we applied the denoising procedure to several 3D-PLI images from a rat and a vervet monkey brain section.