This thesis is focussed on the development of a colour separation methodology for quantification of histopathology stains Ki67 and hematoxylin. Traditional methods require tedious manual evaluations prone to inter and intra-observer variability due to inherent subjectivity. Automatic algorithms have been proposed as the solution to manual challenges, however many algorithms are sensitive to wide staining variability common among histopathology images. This thesis proposes a perceptual colour separation framework that models colour information in a way that mimics the human visual systems ability to identify colour content. It is an automatic framework that was evaluated against 30 manually labelled canine mammary TMA core images. The average difference between manual and automatic PI estimates was 3.25%, with a Kappa similarity statistic of 0.86 across four Ki67 cut-off levels, and a linear correlation coefficient of 0.93. Validation studies show the colour separation framework achieves robust results across various Ki67 staining levels.