High quality rendering and physics based modeling in volume graphics have been limited because intensity based volumetric data do not represent surfaces well. High spatial frequencies due to abrupt intensity changes at object surfaces result in jagged or terraced surfaces in rendered images. The use of a distance-to-closest-surface function to encode object surfaces is proposed. This function varies smoothly across surfaces and hence can be accurately reconstructed from sampled data. The zero value iso surface of the distance map yields the object surface and the derivative of the distance map yields the surface normal. Examples of rendered images are presented along with a new method for calculating distance maps from sampled binary data.

Using distance maps for accurate surface representation in sampled volumes

Volume visualization is a method of extracting meaningful information from volumetric data using interactive graphics and imaging. It is concerned with volume data representation, modeling, manipulation, and rendering. Volume data are 3D (possibly time-varying) entities that may have information inside them, may not consist of tangible surfaces and edges, or may be too voluminous to be represented geometrically. They are obtained by sampling, simulation, or modeling techniques. When volumetric data are visualized using a surface-rendering technique, a dimension of information is essentially lost. In response to this, volume rendering techniques were developed that attempt to capture the entire 3D data in a single 2D image. Volume rendering conveys more information than surface-rendered images but at the cost of increased algorithm complexity and consequently increased rendering times. To improve interactivity in volume rendering, many optimization methods both for software and graphics-accelerator implementations as well as several special-purpose volume rendering machines have been developed.

/pdf/7-overview-of-volume-rendering-3uaii3coqa.pdf

7 – Overview of Volume Rendering

An efficient voxel representation scheme coupled with algorithms for voxelisation, display and thickness analysis of complex free form shapes is presented. The binary voxel model is stored as a stack of layers represented as bit-arrays. Polygonal solid models are converted into voxel format with a maximum resolution of 1000 (one billion voxels) in less than 2 minutes. Near real-time visualization of the voxel model is achieved by direct point rendering using a look-up table based on a finite set of neighbour voxel configurations. Thickness analysis is performed in terms of cross-sectional visualisation, progressive skin removal and radiography with minimum, maximum and total thickness along an orthographic direction. Testing of the algorithms for intricate shapes like Ganesha idol, pelvis bone and automobile cylinder gave satisfactory results. The voxel framework can be coupled to traditional CAD/CAM systems, providing additional capabilities to product engineers.

/pdf/voxel-based-representation-display-and-thickness-analysis-of-2khvvjgex7.pdf

Voxel-based representation, display and thickness analysis of intricate shapes

High-resolution imaging modalities, such as magnetic resonance imaging (MRI) and computer tomography (CT), combined with advances in computer technology, have prompted a renewed interest and have led to significant progress in the volumetric reconstruction of medical images. Assessment of such techniques for various clinical applications and medical educational interpretations are currently under investigation by many medical and scientific groups. The purpose of the paper is to highlight various clinical applications that show potential for the utilization of volumetric rendering of medical images. Such applications include (1) diagnostics, (2) preoperative planning, (3) intraoperative navigation, (4) surgical robotics, (5) postoperative validation, (6) training, and (7) telesurgery. First, however, the authors try to identify the sources of the patients' imaging data, outlining several popular segmentation and volumetric rendering techniques. At the conclusion, they discuss the necessary requirements to deploy a practical three-dimensional (3D) software for clinicians. The paper is intended for those who would like to obtain and general understanding of the clinical 3-D rendering process and its applications. No effort is made at detailing the various volumetric rendering, segmentation, or user interface design techniques, nor is any effort made to review the commercially available software. Although some sample publications are listed to illustrate the various mythologies, no attempt is made at providing a complete coverage of the literature.

Clinical applications of three-dimensional rendering of medical data sets

This paper advocates the use of a group of renderers rather than any specific rendering method. We describe a bundle containing four alternative approaches to visualizing volume data. One new approach uses realistic volumetric gas rendering techniques to produce photo-realistic images and animations. The second uses ray casting that is based on a simpler illumination model and is mainly centered around a versatile new tool for the design of transfer functions. The third method employs a simple illumination model and rapid rendering mechanisms to provide efficient preview capabilities. The last one reduces data magnitude by displaying the most visible components and exploits rendering hardware to provide real time browsing capabilities. We show that each rendering tool provides a unique service and demonstrate the combined utility of our group of volume renderers in computational fluid dynamic (CFD) visualization. While one tool allows the explorer to render rapidly for navigation through the data, another tool allows one to emphasize data features (e.g., shock waves), and yet another tool allows one to realistically render the data. We believe that only through the deployment of groups of renderers will the scientist be well served and equipped to form numerous perspectives of the same dataset, each providing different insights into the data. >

Grouping volume renderers for enhanced visualization in computational fluid dynamics

Presented is a fast display method for volumetric data sets, which involves a slicebased method for extracting potentially visible voxels to represent visible surfaces. For a given viewing direction, the number of visible voxels can be trimmed further by culling most of the voxels not visible from that direction. The entire 3D array of voxels is also present for invasive operations and direct access to interior structures. This approach has been integrated on a low-cost graphic engine as an interactive system for craniofacial surgical planning that is currently in clinical use.

A fast display method for volumetric data

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A fast display method for volumetric data