EM-based segmentation-driven color standardization of digitized histopathology

The development of tools for the processing of color images is often complicated by nonstandardness – the notion that different image regions corresponding to the same tissue will occupy different ranges in the color spectrum. In digital pathology (DP), these issues are often caused by variations in slide thickness, staining, scanning parameters, and illumination. Nonstandardness can be addressed via standardization, a pre-processing step that aims to improve color constancy by realigning color distributions of images to match that of a predefined template image. Unlike color normalization methods, which aim to scale (usually linearly or assuming that the transfer function of the system is known) the intensity of individual images, standardization is employed to align distributions in broad tissue classes (e.g. epithelium, stroma) across different DP images irrespective of institution, protocol, or scanner. Intensity standardization has previously been used for addressing the issue of intensity drift in MRI images, where similar tissue regions have different image intensities across scanners and patients. However, this approach is a global standardization (GS) method that aligns histograms of entire images at once. By contrast, histopathological imagery is complicated by the (a) additional information present in color images and (b) heterogeneity of tissue composition. In this paper, we present a novel color Expectation Maximization (EM) based standardization (EMS) scheme to decompose histological images into independent tissue classes (e.g. nuclei, epithelium, stroma, lumen) via the EM algorithm and align the color distributions for each class independently. Experiments are performed on prostate and oropharyngeal histopathology tissues from 19 and 26 patients, respectively. Evaluation methods include (a) a segmentation-based assessment of color consistency in which normalized median intensity (NMI) is calculated from segmented regions across a dataset and (b) a quantitative measure of histogram alignment via mean landmark distance. EMS produces lower NMI standard deviations (i.e. greater consistency) of 0.0054 and 0.0034 for prostate and oropharyngeal cohorts, respectively, than unstandardized (0.034 and 0.026) and GS (0.031 and 0.017) approaches. Similarly, we see decreased mean landmark distance for EMS (2.25 and 4.20) compared to both unstandardized (54.8 and 27.3) and GS (27.1 and 8.8) images. These results suggest that the separation of broad tissue classes in EMS is vital to the standardization of DP imagery and subsequent development of computerized image analysis tools.