The colour of disease by 7.4 Limited
Online images are an important resource for understanding medical conditions and their impact on those affected. The images of disease available online are dominated by visible effects on the human body, imaging and diagnosis techniques, transmission vectors, media coverage, awareness campaigns, and cultural and psychological associations. These factors influence the colours present in images of disease, and colour plays an important role in our perception of and emotional response to images and their subject matter.
To investigate the association between colour and disease, we analysed the colour palette for 100 conditions based on the images available online.
Some conditions are dominated by the colours of strong awareness campaigns: breast cancer is pink, prostate cancer is blue, and mitochondrial disease is green. Multiple images of blood cells make conditions like haemophilia and sickle cell anaemia red, while conditions like chicken pox and measles are dominated by flesh tones. And of course, scarlet fever is red and yellow fever is yellow. Polio is almost completely monochrome due to the number of black and white images predating the widespread use of poliovirus vaccine in the 1950s. Diagnostic images for ophthalmologic conditions like glaucoma and macular degeneration are typically orange, while MRI scans used to diagnose adrenoleukodystrophy (ALD) are predominantly black and white. Cultural and psychological associations also seem to be important, especially for mental health conditions: depression is black and blue, while anxiety is dark with multi-coloured flashes.
The visuals presented here show the subtle way in which colour in the environment can infiltrate our subconscious and influence our individual perceptions of these conditions.
Methods: For each condition, we retrieved the first 100 images from a Google search (July 2017). Image size and shape were standardized and the images were combined. The pixels of the combined image were sorted by colour intensity (RGB values summed and divided by 3; https://github.com/fragmer/PixelSorter), and polar plots constructed using Adobe Photoshop.
CreditsBen Dean, Helen Dell, Marina Sciberras, Hannah Bowell, Malini Ladd, Matt Evans