  Due to recent advances in technology, digitized histopathology images are now
widely available for both clinical and research purposes. Accordingly, research
into computerized image analysis algorithms for digital histopathology images
has been progressing rapidly. In this work, we focus on image retrieval for
digital histopathology images. Image retrieval algorithms can be used to find
similar images and can assist pathologists in making quick and accurate
diagnoses. Histopathology images are typically stained with dyes to highlight
features of the tissue, and as such, an image analysis algorithm for
histopathology should be able to process colour images and determine relevant
information from the stain colours present. In this study, we are interested in
the effect that stain separation into their individual stain components has on
image search performance. To this end, we implement a basic k-nearest
neighbours (kNN) search algorithm on histopathology images from two publicly
available data sets (IDC and BreakHis) which are: a) converted to greyscale, b)
digitally stain-separated and c) the original RGB colour images. The results of
this study show that using H\&E separated images yields search accuracies
within one or two percent of those obtained with original RGB images, and that
superior performance is observed using the H\&E images in most scenarios we
tested.
