As image analysis expands into clinical and basic applications it is important that users be aware of opportunities and limitations. A common image analysis workflow involves the digitization of stained tissue sections into a red-green-blue (RGB) colour model for quantitative interpretation. Upstream of the digital image, quality and variability can be degraded at each step (tissue handling, fixation, sectioning, staining, image acquisition). Digital image analysis presents additional steps where variables can affect data quality. Image analysis platforms are not uniform. Aside from interface preferences, some introduce unintended variability due to their processing architecture that may not be obvious to the end-user. One important component of this is colour space representation: hue-saturation-intensity (HSI) vs. colour deconvolution (CD). A potential weakness of analyses within the HSI colour space is the mis-identification of darkly stained pixels, particularly when more than one stain is present. We were interested to discover whether HSI or CD provided greater fidelity in a typical immunoperoxidase/hematoxylin dataset.
Fifty-nine samples were processed using HSI- and CD-based analyses. Processed image pairs were compared with the original sample to determine which processed image provided a more accurate representation. CD proved superior to HSI in 94.9% of the analyzed image pairs. Where the option exists, CD-based image analysis is strongly recommended.
This presentation will enable the learner to: