Staining Correction in Digital Pathology by Utilizing a Dye Amount Table

• Published in: Journal of digital imaging Vol. 28; no. 3; pp. 283 - 294
• Main Authors: Bautista, Pinky A., Yagi, Yukako
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2015; Springer Nature B.V
• Subjects: Color; Coloring Agents; Histological Techniques - methods; Humans; Imaging; Medicine; Medicine & Public Health; Microscopy; Radiology; Staining and Labeling; Digital pathology; Look-up table; Staining normalization; Dye amount; Staining correction; Color correction
• ISSN: 0897-1889; 1618-727X
• DOI: 10.1007/s10278-014-9766-0

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.