Abstract
BackgroundDirect volume rendering is one of flexible and effective approaches to inspect large volumetric data such as medical and biological images. In conventional volume rendering, it is often time consuming to set up a meaningful illumination environment. Moreover, conventional illumination approaches usually assign same values of variables of an illumination model to different structures manually and thus neglect the important illumination variations due to structure differences.ResultsWe introduce a novel illumination design paradigm for volume rendering on the basis of topology to automate illumination parameter definitions meaningfully. The topological features are extracted from the contour tree of an input volumetric data. The automation of illumination design is achieved based on four aspects of attenuation, distance, saliency, and contrast perception. To better distinguish structures and maximize illuminance perception differences of structures, a two-phase topology-aware illuminance perception contrast model is proposed based on the psychological concept of Just-Noticeable-Difference.ConclusionsThe proposed approach allows meaningful and efficient automatic generations of illumination in volume rendering. Our results showed that our approach is more effective in depth and shape depiction, as well as providing higher perceptual differences between structures.
Highlights
Direct volume rendering is one of flexible and effective approaches to inspect large volumetric data such as medical and biological images
We see that all renderings (Fig. 3d–f ) from approaches presented in this paper show internal structures more clearly than other approaches by emphasising internal structures from the illumination perspective
This paper introduced a new paradigm for illumination design in volume rendering based on data topology
Summary
Direct volume rendering is one of flexible and effective approaches to inspect large volumetric data such as medical and biological images. Conventional illumination approaches usually assign same values of variables of an illumination model to different structures manually and neglect the important illumination variations due to structure differences. Illumination design in volume rendering has been investigated extensively from different perspectives, such as optical model design [6], lighting optimization based on perception [7] and structure [4]. Despite the concept of lighting transfer function [9] trying to vary lighting coefficients of a local illumination model based on gradient information, similar to conventional opacity transfer functions based on gradients [10], it still cannot depict differences of various structures effectively from the illumination perspective. In order to depict perception and importance differences between structures from the illumination perspective in volume rendering, a new illumination approach is highly desirable. It is often time consuming to define meaningful illumination parameters for visualization even for experienced users
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