Colorimetrical evaluation of color normalization methods for H&E-stained images

Color normalization is one of the pre-processing steps employed by many deep learning-based algorithms used for aiding pathology diagnoses with whole-slide images. Due to variability in tissue type, specimen preparation, staining protocol, and scanner performance, whole-slide images acquired from different sources may exhibit pronounced color variability that hinders algorithms from executing effectively. In the literature, numerous methods have been proposed to colornormalize hematoxylin and eosin (H&E)-stained images. However, the objective of color normalization has not been colorimetrically defined or evaluated beyond visual comparison. In this study, a quantitative metric, color normality, was defined to evaluate the degree of color similarity between images involved in a color normalization process. The pixelwise spectral data of eight H&E-stained tissue slides were optically measured as the ground truth to test the Reinhard, Macenko, and Vahadane methods. Principal component analysis was conducted on the spectral data to derive a new color normalization method as the reference. Experiment results show that the H&E color gamut needs to be expressed with three components, but the widely used Macenko and Vahadane methods compressed the three-dimensional color gamut volume into a two-dimensional surface and reduced color gamut volumes by 40% or more. None of the color normalization methods could achieve a color normality of greater than 0.6174 when the image was not self-normalized.