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.
MeSH terms
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Algorithms*
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Breast Neoplasms / diagnosis
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Breast Neoplasms / metabolism
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Breast Neoplasms / pathology
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Colonic Neoplasms / diagnosis
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Colonic Neoplasms / metabolism
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Colonic Neoplasms / pathology
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Color*
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Coloring Agents / pharmacokinetics*
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Eosine Yellowish-(YS) / pharmacokinetics
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Female
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Hematoxylin / pharmacokinetics
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Histological Techniques / methods*
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Histological Techniques / statistics & numerical data
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Humans
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Image Processing, Computer-Assisted / methods*
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Lung Neoplasms / diagnosis
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Lung Neoplasms / metabolism
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Lung Neoplasms / pathology
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Models, Statistical*
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Staining and Labeling / methods
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Staining and Labeling / statistics & numerical data
Substances
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Coloring Agents
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Eosine Yellowish-(YS)
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Hematoxylin
Grants and funding
Najah Alsubaie is being funded by Saudi Ministry of Education, Princess Nourah University, Riyadh, KSA. This funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.