ArticleJournal of digital imaging2015
Staining correction in digital pathology by utilizing a dye amount table.
Article in Journal of digital imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification.Journal of imaging informatics in medicine · 2024Article
- Fractal-Based Analysis of Histological Features of Brain Tumors.Advances in neurobiology · 2024Article
- Rapid Remote Online Evaluation in Endoscopic Diagnostics: An Analysis of Biopsy-Proven Respiratory Cytopathology.Diagnostics (Basel, Switzerland) · 2023Article
- Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology.Brain pathology (Zurich, Switzerland) · 2022Article
- Tutorial: methods for three-dimensional visualization of archival tissue material.Nature protocols · 2021Review
- Review
- Normalization of HE-stained histological images using cycle consistent generative adversarial networks.Diagnostic pathology · 2021Article
- Image-to-image translation for automatic ink removal in whole slide images.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Color standardization and optimization in whole slide imaging.Clinical and diagnostic pathology · 2020Article
- Display Characteristics and Their Impact on Digital Pathology: A Current Review of Pathologists' Future "Microscope".Journal of pathology informatics · 2020Review
- New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.PloS one · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The stained colors of the tissue components are popularly used as features for image analysis. However, variations in the staining condition of the histology slides prompt variations to the color distribution of the stained tissue samples which could impact the accuracy of the analysis. In this paper, we present a method to correct the staining condition of a histology image. In the method, a look-up table (LUT) based on the dye amounts absorbed by the sample is built. The LUT can be built when either (i) the source and reference staining conditions are specified or (ii) when the user simply wants to recreate his/her preferred staining condition without specifying any reference slide. The effectiveness of the present method was evaluated in two aspects: (i) CIELAB color difference of nuclei, cytoplasm, and red blood cells, between the ten different slides of liver tissue, and (ii) classification of the different tissue components. Application of the present staining correction method reduced the color difference between the slides by an average factor of 9.8 and the classification performance of a linear discriminant classifier improved by 16.5% on the average. Results of the paired t test statistical analysis further showed that the reduction in the CIELAB color difference between the slides and the improvement in the classifier's performance when staining correction was implemented is significant at p < 0.001.
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