Literature DB >> 20946383

Digital stain separation for histological images.

P J Tadrous1.   

Abstract

It is often desirable to perform digital image analyses on sections prepared for human interpretation, e.g. nuclear chromatin texture analysis or three-dimensional reconstructions using sections requiring human delineation of structures of interest. Unfortunately such analyses are often more effective using stains with less complex contrast. Here an automated selective 'de-staining' method for digital images is presented. The method separates an image into its red, green and blue and hue, saturation and intensity components. A mask of stained tissue is prepared by automatic percentile thresholding. A single weighted inverted colour channel is then added to each of the three primary colour channels separately by an iterative algorithm that adjusts the weights to give minimum variance within the mask. The modified red, green and blue channels are then recombined. This method is automatic requiring no pre-definition of stain colours or special hardware. The method is demonstrated to 'de-stain' nuclei in haematoxylin and eosin (H&E) sections (and a separate haematoxylin image can be derived from this). An image of isolated brown reaction product is produced with immunoperoxidase preparations counterstained with haematoxylin. Furthermore trichrome (haematoxylin van Gieson, picrosirius red) and other common stains may be separated into their components with modifications of the same algorithm. Although other methods for colour separation do exist (e.g. spectral pathology and colour deconvolution) these require special apparatus or precise calibration and foreknowledge of pure dye colour spectra. The present method of digital stain separation is fully automatic with no such prerequisites.
© 2010 The Author Journal compilation © 2010 The Royal Microscopical Society.

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Year:  2010        PMID: 20946383     DOI: 10.1111/j.1365-2818.2010.03390.x

Source DB:  PubMed          Journal:  J Microsc        ISSN: 0022-2720            Impact factor:   1.758


  8 in total

1.  Digital separation of diaminobenzidine-stained tissues via an automatic color-filtering for immunohistochemical quantification.

Authors:  Rong Fu; Xiaomian Ma; Zhaoying Bian; Jianhua Ma
Journal:  Biomed Opt Express       Date:  2015-01-15       Impact factor: 3.732

2.  Application of alternative fixatives to formalin in diagnostic pathology.

Authors:  L Benerini Gatta; M Cadei; P Balzarini; S Castriciano; R Paroni; A Verzeletti; V Cortellini; F De Ferrari; P Grigolato
Journal:  Eur J Histochem       Date:  2012-05-04       Impact factor: 3.188

3.  High-definition hematoxylin and eosin staining in a transition to digital pathology.

Authors:  Jamie D Martina; Christopher Simmons; Drazen M Jukic
Journal:  J Pathol Inform       Date:  2011-10-19

4.  Enabling Histopathological Annotations on Immunofluorescent Images through Virtualization of Hematoxylin and Eosin.

Authors:  Amal Lahiani; Eldad Klaiman; Oliver Grimm
Journal:  J Pathol Inform       Date:  2018-02-14

5.  Validation of various adaptive threshold methods of segmentation applied to follicular lymphoma digital images stained with 3,3'-Diaminobenzidine&Haematoxylin.

Authors:  Anna Korzynska; Lukasz Roszkowiak; Carlos Lopez; Ramon Bosch; Lukasz Witkowski; Marylene Lejeune
Journal:  Diagn Pathol       Date:  2013-03-25       Impact factor: 2.644

6.  Histological stain evaluation for machine learning applications.

Authors:  Jimmy C Azar; Christer Busch; Ingrid B Carlbom
Journal:  J Pathol Inform       Date:  2013-03-30

7.  Current breast cancer proliferative markers correlate variably based on decoupled duration of cell cycle phases.

Authors:  Lik Hang Lee; Hua Yang; Gilbert Bigras
Journal:  Sci Rep       Date:  2014-05-30       Impact factor: 4.379

8.  A new image-based tool for the high throughput phenotyping of pollen viability: evaluation of inter- and intra-cultivar diversity in grapevine.

Authors:  Javier Tello; María Ignacia Montemayor; Astrid Forneck; Javier Ibáñez
Journal:  Plant Methods       Date:  2018-01-09       Impact factor: 4.993

  8 in total

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