Grayscale representation of infrared microscopy images by extended multiplicative signal correction for registration with histological images. Issue 8 (11th May 2020)

• Record Type: Journal Article
• Authors: Trukhan, Stanislau; Tafintseva, Valeria; Tøndel, Kristin; Großerueschkamp, Frederik; Mosig, Axel; Kovalev, Vassili; Gerwert, Klaus; Kohler, Achim
• Is Part Of: Journal of biophotonics. Volume 13:Issue 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-11
• Subjects: biospectroscopy -- EMSC -- FTIR -- H&E -- image registration -- infrared spectroscopy -- microscopy; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201960223
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: Fourier‐transform infrared (FTIR) microspectroscopy is rounding the corner to become a label‐free routine method for cancer diagnosis. In order to build infrared‐spectral based classifiers, infrared images need to be registered with Hematoxylin and Eosin (H&E) stained histological images. While FTIR images have a deep spectral domain with thousands of channels carrying chemical and scatter information, the H&E images have only three color channels for each pixel and carry mainly morphological information. Therefore, image representations of infrared images are needed that match the morphological information in H&E images. In this paper, we propose a novel approach for representation of FTIR images based on extended multiplicative signal correction highlighting morphological features that showed to correlate well with morphological information in H&E images. Based on the obtained representations, we developed a strategy for global‐to‐local image registration for FTIR images and H&E stained histological images of parallel tissue sections. Abstract : Grayscale representation of Fourier‐transform infrared (FTIR) microscopy images and its application to image registration with Hematoxylin and Eosin (H&E) stained histological images are presented. The proposed image registration strategy allows transferring annotations from histological images to infrared microscopy images in order to establish a ground truth for building supervised learning algorithms on infrared images. Based on FTIR‐to‐grayscale and H&E‐to‐grayscale representations, a strategy for global‐to‐local image registration for FTIR images and H&E stained histological images of parallel tissue sections is developed.