A transfer function optimization using visual saliency for region of interest-based direct volume rendering

초록

Direct volume rendering (DVR) helps data interpretation by allowing users to focus attention on specific regions in a volume that are of most interest. The effective generation of these regions of interest (ROIs)-based DVR images, however, still remains a major challenge. It involved the intricate process of assigning appropriate optical parameters (opacity and/or color) to the ROIs and other regions in the volume using transfer function (TF) specification. Achieving an optimal TF often requires multiple iterations of a trial-and-error process, starting from scratch in each case, which is time-consuming and labor-intensive. To address this issue, there have been various TF optimization approaches, which could automatically fine-tune an initial TF toward an optimal solution satisfying a pre-defined objective metric. Although effective, the previous objective metrics exhibit two inherent limitations: (i) they were only capable of optimizing ROIs along an individual viewing ray of the volume, and the relationship with neighboring ROIs (as context) from other rays could not be controlled; (ii) the TF parameters to be optimized were restricted to opacity (i.e., the observability of ROIs) and did not include color which is also important to enhance the visual distinction of ROIs. In this work, we propose a new TF optimization approach by introducing an objective metric using visual saliency. Visual saliency is a biologically-inspired measure to aid the identification of ROIs considered important relative to other regions within an image according to the human visual system. Our novel adoption of visual saliency enables the TF optimization to take into account all ROIs in a volume (with its rendered image) implicitly. In addition, both opacity and color parameters can be applied to the TF optimization. Given the user-assigned target visual saliency of ROIs, our approach can produce more controllable and complete ROIs-based DVR images. We outline our approach by applications to a variety of volumetric datasets and highlight its advantages in comparison to the dominant TF optimization approaches that use visibility-based objective metrics.

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