Logo image
Back
An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides
Journal article   Open access   Peer reviewed

An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides

Mark D Zarella, David E Breen, Andrei Plagov and Fernando U Garcia
Journal of pathology informatics, v 6(1), pp 33-33
01 Jan 2015
DOI: https://doi.org/10.4103/2153-3539.158910
PMID: 26167377
url
https://doi.org/10.4103/2153-3539.158910View
Published, Version of Record (VoR) Open

Abstract

image processing Cell counting segmentation classification machine learning
Hematoxylin and eosin (H&E) staining is ubiquitous in pathology practice and research. As digital pathology has evolved, the reliance of quantitative methods that make use of H&E images has similarly expanded. For example, cell counting and nuclear morphometry rely on the accurate demarcation of nuclei from other structures and each other. One of the major obstacles to quantitative analysis of H&E images is the high degree of variability observed between different samples and different laboratories. In an effort to characterize this variability, as well as to provide a substrate that can potentially mitigate this factor in quantitative image analysis, we developed a technique to project H&E images into an optimized space more appropriate for many image analysis procedures. We used a decision tree-based support vector machine learning algorithm to classify 44 H&E stained whole slide images of resected breast tumors according to the histological structures that are present. This procedure takes an H&E image as an input and produces a classification map of the image that predicts the likelihood of a pixel belonging to any one of a set of user-defined structures (e.g., cytoplasm, stroma). By reducing these maps into their constituent pixels in color space, an optimal reference vector is obtained for each structure, which identifies the color attributes that maximally distinguish one structure from other elements in the image. We show that tissue structures can be identified using this semi-automated technique. By comparing structure centroids across different images, we obtained a quantitative depiction of H&E variability for each structure. This measurement can potentially be utilized in the laboratory to help calibrate daily staining or identify troublesome slides. Moreover, by aligning reference vectors derived from this technique, images can be transformed in a way that standardizes their color properties and makes them more amenable to image processing.

Metrics

12 Record Views

Details

Logo image