Abstract
A common problem in visual appearance research is how to quantitatively characterise the visual appearance of a region of an image which is categorised by human observers in the same way. An example of this is scarring in medical images (Ayoub et al, 2010, The Cleft-Palate Craniofacial Journal, in press). We have argued that “scarriness” is itself a visual appearance descriptor which summarises the distinctive combination of colour, texture and shape information which allows us to distinguish scarred from non-scarred tissue (Simmons et al, ECVP 2009). Other potential descriptors for other image classes would be “metallic”, “natural”, or “liquid”. Having developed an automatic algorithm to locate scars in medical images, we then tested “ground truth” by asking untrained observers to draw around the region of scarring. The shape and size of the scar on the image was defined by building a contour plot of the agreement between observers' outlines and thresholding at the point above which 50% of the observers agreed: a consensus coding scheme. Based on the variability in the amount of overlap between the scar as defined by the algorithm, and the consensus scar of the observers, we have concluded that the algorithm does not completely capture the putative appearance descriptor “scarriness”. A simultaneous analysis of qualitative descriptions of the scarring by the observers revealed that other image features than those encoded by the algorithm (colour and texture) might be important, such as scar boundary shape. This approach to visual appearance research in medical imaging has potential applications in other application areas, such as botany, geology and archaeology.