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Semantic Visualization Facilitated By Cluster Analysis

Volumetric data offer considerably more information for analysis than the density and gradient values most often used in transfer function guided feature detection and enhancement. There is a wide array of further metrics that can be computed to reveal additional useful information for this task. However, the introduction of a large number of metrics is challenging in that it creates a high-dimensional classification space which is impossible to handle within the present popular transfer function interface paradigm. We therefore propose to use cluster analysis for this task, in conjunction with an effective and intuitive user interface to shape volume features of interest within a dualdomain interaction framework. Our paper presents some initial results accomplished with our interface.