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Stain deconvolution using statistical analysis of multi-resolution stain colour representation
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Alsubaie, Najah, Trahearn, Nicholas, Raza, Shan-e-Ahmed, Snead, David and Rajpoot, Nasir M. (2017) Stain deconvolution using statistical analysis of multi-resolution stain colour representation. PLoS One, 12 (1). e0169875. doi:10.1371/journal.pone.0169875
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Official URL: http://dx.doi.org/10.1371/journal.pone.0169875
Abstract
Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QM Human anatomy | ||||||||
| Divisions: | Faculty of Medicine > Warwick Medical School > Biomedical Sciences Faculty of Science > Computer Science Faculty of Medicine > Warwick Medical School |
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| Library of Congress Subject Headings (LCSH): | Histology -- Technique, Diagnostic imaging | ||||||||
| Journal or Publication Title: | PLoS One | ||||||||
| Publisher: | Public Library of Science | ||||||||
| ISSN: | 1932-6203 | ||||||||
| Official Date: | 11 January 2017 | ||||||||
| Dates: |
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| Volume: | 12 | ||||||||
| Number: | 1 | ||||||||
| Article Number: | e0169875 | ||||||||
| DOI: | 10.1371/journal.pone.0169875 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Funder: | Saudi Arabia. Wizārat al-Maʻārif [Saudi Arabia. Ministry of Education], Princess Nora bint Abdul Rahman University |
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