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Quantifying visual similarity in clinical iconic graphics.

Philip R O Payne1, Justin B Starren

  • 1Department of Biomedical Informatics, Columbia University, 622 West 168th Street, VC5, New York, NY 10025, USA. philip.payne@dbmi.columbia.edu

Journal of the American Medical Informatics Association : JAMIA
|February 3, 2005
PubMed
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This study introduces Presentation Discovery, a novel method to measure visual similarity of graphical elements. It enables more informed icon design by ensuring users perceive shapes consistently, improving user interface clarity.

Area of Science:

  • Human-Computer Interaction
  • Visual Perception
  • Information Design

Background:

  • Icons and graphical elements are integral to modern user interfaces.
  • Effective icon interpretation relies on shared user perception of visual similarity.
  • Measuring this similarity is key to designing intuitive graphical components.

Purpose of the Study:

  • To evaluate Presentation Discovery, a new method for assessing visual similarity of graphical primitives.
  • To apply this method within the mammography domain to categorize visual representations of findings.
  • To establish a data-driven approach for icon design.

Main Methods:

  • Domain experts graphically represented 50 mammography findings.
  • Non-domain experts sorted these graphics based on visual similarity.

Related Experiment Videos

  • Hierarchical clustering and hypothesis discovery tools analyzed sorting agreement and identified consensus clusters.

    • Main Results:

    • Multiple non-domain experts reliably grouped graphics, correlating strongly with underlying mammography concepts.
    • Analysis revealed graphical primitives suitable for informative icon design.
    • The method demonstrated a quantitative measure of visual agreement.

    Conclusions:

    • Presentation Discovery offers a rigorous, data-driven alternative to intuitive icon design.
    • The method can enhance the creation of user interface graphical components.
    • Consistent visual perception of icons can be systematically achieved.