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dc.contributor.authorTrukhan, Stanislau
dc.contributor.authorTafintseva, Valeria
dc.contributor.authorTøndel, Kristin
dc.contributor.authorGroßerueschkamp, Frederik
dc.contributor.authorMosig, Axel
dc.contributor.authorKovalev, Vassili
dc.contributor.authorGerwert, Klaus
dc.contributor.authorKohler, Achim
dc.date.accessioned2021-11-17T10:46:23Z
dc.date.available2021-11-17T10:46:23Z
dc.date.created2020-10-05T09:54:06Z
dc.date.issued2020
dc.identifier.issn1864-063X
dc.identifier.urihttps://hdl.handle.net/11250/2830069
dc.description.abstractFourier-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.
dc.language.isoeng
dc.titleGrayscale representation of infrared microscopy images by Extended Multiplicative Signal Correction for registration with histological images
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.source.journalJournal of Biophotonics
dc.identifier.doi10.1002/jbio.201960223
dc.identifier.cristin1836962
dc.relation.projectNorges forskningsråd: 289518
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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