Abstract:
Digital imaging of H&E stained slides has enabled the application of
image processing to support pathology workflows. Potential applications
include computer-aided diagnostics, advanced quantification tools, and
innovative visualization platforms. However, the intrinsic variability
of biological tissue and the vast differences in tissue preparation
protocols often lead to significant image variability that can hamper
the effectiveness of these computational tools. We developed an
alternative representation for H&E images that operates within a space
that is more amenable to many of these image processing tools. The
algorithm to derive this representation operates by exploiting the
correlation between color and the spatial properties of the biological
structures present in most H&E images. In this way, images are transformed
into a structure-centric space in which images are segregated into tissue
structure channels. We demonstrate that this framework can be extended to
achieve color normalization, effectively reducing inter-slide variability.
