Registration of Standardized Histological Images in Feature Space

TL;DR

This paper introduces three novel methods for histological image registration, including intensity standardization, feature space mapping, and automatic reference slice selection, to improve 3D reconstruction accuracy.

Contribution

The paper presents new techniques for intensity correction, feature-based registration, and reference slice selection that enhance histological 3D reconstruction quality.

Findings

Intensity standardization improves tissue similarity mapping.

Feature space mapping enhances registration accuracy.

Automatic reference slice selection reduces reconstruction errors.

Abstract

In this paper, we propose three novel and important methods for the registration of histological images for 3D reconstruction. First, possible intensity variations and nonstandardness in images are corrected by an intensity standardization process which maps the image scale into a standard scale where the similar intensities correspond to similar tissues meaning. Second, 2D histological images are mapped into a feature space where continuous variables are used as high confidence image features for accurate registration. Third, we propose an automatic best reference slice selection algorithm that improves reconstruction quality based on both image entropy and mean square error of the registration process. We demonstrate that the choice of reference slice has a significant impact on registration error, standardization, feature space and entropy information. After 2D histological slices are registered through an affine transformation with respect to an automatically chosen reference, the 3D volume is reconstructed by co-registering 2D slices elastically.