An empirical study of data visualization techniques in PACS design

Abstract

The paper presents an empirical study of multidimensional visualization techniques. The study is motivated by the problem of decision making in PACS (Picture Archiving and Communications System) design. A comprehensive survey of visualizations used in literature is performed and these survey results are then used to produce the final set of considered visualizations: tables (as control), scatterplots, parallel coordinates, and star plots. An electronic testing tool is developed to present visualizations to three sets of experimental subjects in order to determine which visualization technique allows users to make the correct decision in a sample decision making problem based on real-world data. Statistical analysis of the results demonstrates that visualizations show better results in decision support than tables. Further, when number of dimensions is large, 2D parallel coordinates show the best results in accuracy. The contribution of the presented research operates on two levels of abstraction. On the object level, it provides useful data regarding the relative merits of visualization techniques for the considered narrow use-case, which can then be generalized to other similar problem sets. On the meta level above, it contributes an enhanced methodology to the area of empirical visualization evaluation methods.