Quantifying visual similarity in clinical iconic graphics

The use of icons and other graphical components in user interfaces has become nearly ubiquitous. The interpretation of such icons is based on the assumption that different users perceive the shapes similarly. At the most basic level, different users must agree on which shapes are similar and which are different. If this similarity can be measured, it may be usable as the basis to design better icons.

The purpose of this study was to evaluate a novel method for categorizing the visual similarity of graphical primitives, called Presentation Discovery, in the domain of mammography. Six domain experts were given 50 common textual mammography findings and asked to draw how they would represent those findings graphically. Nondomain experts sorted the resulting graphics into groups based on their visual characteristics. The resulting groups were then analyzed using traditional statistics and hypothesis discovery tools. Strength of agreement was evaluated using computational simulations of sorting behavior.

Sorter agreement was measured at both the individual graphical and concept-group levels using a novel simulation-based method. "Consensus clusters" of graphics were derived using a hierarchical clustering algorithm.

The multiple sorters were able to reliably group graphics into similar groups that strongly correlated with underlying domain concepts. Visual inspection of the resulting consensus clusters indicated that graphical primitives that could be informative in the design of icons were present.

The method described provides a rigorous alternative to intuitive design processes frequently employed in the design of icons and other graphical interface components.