Role of domain knowledge in developing user-centered medical-image indexing

  • Authors:

  • Xin Wang;Sanda Erdelez;Carla Allen;Blake Anderson;Hongfei Cao;Chi-Ren Shyu

  • Affiliations:

  • School of Information Science and Learning Technologies, University of Missouri, 303 Townsend Hall, Columbia, MO 65211;School of Information Science and Learning Technologies, University of Missouri, 303 Townsend Hall, Columbia, MO 65211;School of Health Professions, University of Missouri, 619 Lewis Hall, Columbia, MO 65211;Informatics Institute, University of Missouri, 241 Engineering Building West, Columbia, MO 65211;Department of Computer Science, University of Missouri, 238 Engineering Building West, Columbia, MO 65201;Informatics Institute, University of Missouri, 241 Engineering Building West, Columbia, MO 65211

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2012

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Abstract

An efficient and robust medical-image indexing procedure should be user-oriented. It is essential to index the images at the right level of description and ensure that the indexed levels match the user's interest level. This study examines 240 medical-image descriptions produced by three different groups of medical-image users (novices, intermediates, and experts) in the area of radiography. This article reports several important findings: First, the effect of domain knowledge has a significant relationship with the use of semantic image attributes in image-users' descriptions. We found that experts employ more high-level image attributes which require high-reasoning or diagnostic knowledge to search for a medical image (Abstract Objects and Scenes) than do novices; novices are more likely to describe some basic objects which do not require much radiological knowledge to search for an image they need (Generic Objects) than are experts. Second, all image users in this study prefer to use image attributes of the semantic levels to represent the image that they desired to find, especially using those specific-level and scene-related attributes. Third, image attributes generated by medical-image users can be mapped to all levels of the pyramid model that was developed to structure visual information. Therefore, the pyramid model could be considered a robust instrument for indexing medical imagery. © 2012 Wiley Periodicals, Inc.