A method for normalizing pathology images to improve feature extraction for quantitative pathology. Issue 1 (5th January 2016)

• Record Type: Journal Article
• Authors: Tam, Allison; Barker, Jocelyn; Rubin, Daniel
• Is Part Of: Medical physics. Volume 43:Issue 1(2016)
• Page Start: 528
• Page End: 537
• Publication Date: 2016-01-05
• Subjects: feature extraction -- lung -- medical image processing -- tumours; Medical imaging; Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; image processing -- digital pathology -- normalization; Pathology -- Cancer -- Tissues -- Image analysis -- Medical image segmentation -- Medical image artifacts -- Proteins -- Tissue engineering -- Lungs; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4939130
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: With the advent of digital slide scanning technologies and the potential proliferation of large repositories of digital pathology images, many research studies can leverage these data for biomedical discovery and to develop clinical applications. However, quantitative analysis of digital pathology images is impeded by batch effects generated by varied staining protocols and staining conditions of pathological slides. Methods: To overcome this problem, this paper proposes a novel, fully automated stain normalization method to reduce batch effects and thus aid research in digital pathology applications. Their method, intensity centering and histogram equalization (ICHE), normalizes a diverse set of pathology images by first scaling the centroids of the intensity histograms to a common point and then applying a modified version of contrast‐limited adaptive histogram equalization. Normalization was performed on two datasets of digitized hematoxylin and eosin (H&E) slides of different tissue slices from the same lung tumor, and one immunohistochemistry dataset of digitized slides created by restaining one of the H&E datasets. Results: The ICHE method was evaluated based on image intensity values, quantitative features, and the effect on downstream applications, such as a computer aided diagnosis. For comparison, three methods from the literature were reimplemented and evaluated using the same criteria. The authors found that ICHE not only improved performance compared with un‐normalized images, but in most cases showed improvement compared with previous methods for correcting batch effects in the literature. Conclusions: ICHE may be a useful preprocessing step a digital pathology image processing pipeline.