VolumeEVM: A new surface/volume integrated model

Volume visualization is a very active research area in the field of scien-tific visualization. The Extreme Vertices Model (EVM) has proven to be a complete intermediate model to visualize and manipulate volume data using a surface rendering approach. However, the ability to integrate the advantages of surface rendering approach with the superiority in visual exploration of the volume rendering would actually produce a very complete visualization and edition system for volume data. Therefore, we decided to define an enhanced EVM-based model which incorporates the volumetric information required to achieved a nearly direct volume visualization technique. Thus, VolumeEVM was designed maintaining the same EVM-based data structure plus a sorted list of density values corresponding to the EVM-based VoIs interior voxels. A function which relates interior voxels of the EVM with the set of densities was mandatory to be defined. This report presents the definition of this new surface/volume integrated model based on the well known EVM encoding and propose implementations of the main software-based direct volume rendering techniques through the proposed model.Postprint (published version

Drishti, a volume exploration and presentation tool

Among several rendering techniques for volumetric data, direct volume rendering is a powerful visualization tool for a wide variety of applications. This paper describes the major features of hardware based volume exploration and presentation tool - Drishti. The word, Drishti, stands for vision or insight in Sanskrit, an ancient Indian language. Drishti is a cross-platform open-source volume rendering system that delivers high quality, state of the art renderings. The features in Drishti include, though not limited to, production quality rendering, volume sculpting, multi-resolution zooming, transfer function blending, profile generation, measurement tools, mesh generation, stereo/anaglyph/crosseye renderings. Ultimately, Drishti provides an intuitive and powerful interface for choreographing animations

Photo-realistic image synthesis in volume rendering for rapid prototyping and CAM applications (draft)

This document describes some preliminary results of the application of direct volume visualization and realistic image synthesis techniques in Computer Aided Manufacturing (CAM) field. The final goal is to analyze the use volume data representation and direct volume visualization techniques respectively as basic geometric element and rendering method for numerical control (NC) process planning and simulation. In particular we want to study their application in the process of human organs replication by rapid prototyping techniques. Volume rendering techniques will be used to evaluate the geometric reconstruction of volumetric data (e.g. parts of human body extracted from a CT scan as the carotid arteries), in order to obtain the best polygonal approximation of a dataset. The polygonal model resulting from this evaluation stage will be used to drive its physical reconstruction via rapid prototyping. A volume rendering program, called VolCastIA, is presented. VolCastIA is a software environment built to evaluate the use of state of the art image synthesis methods in the volume rendering context

Volume ray casting techniques and applications using general purpose computations on graphics processing units

Traditional 3D computer graphics focus on rendering the exterior of objects. Volume rendering is a technique used to visualize information corresponding to the interior of an object, commonly used in medical imaging and other fields. Visualization of such data may be accomplished by ray casting; an embarrassingly parallel algorithm also commonly used in ray tracing. There has been growing interest in performing general purpose computations on graphics processing units (GPGPU), which are capable exploiting parallel applications and yielding far greater performance than sequential implementations on CPUs. Modern GPUs allow for rapid acceleration of volume rendering applications, offering affordable high performance visualization systems. This thesis explores volume ray casting performance and visual quality enhancements using the NVIDIA CUDA platform, and demonstrates how high quality volume renderings can be produced with interactive and real time frame rates on modern commodity graphics hardware. A number of techniques are employed in this effort, including early ray termination, super sampling and texture filtering. In a performance comparison of a sequential versus CUDA implementation on high-end hardware, the latter is capable of rendering 60 frames per second with an impressive price-performance ratio heavily favoring GPUs. A number of unique volume rendering applications are explored including multiple volume rendering capable of arbitrary placement and rigid volume registration, hypertexturing and stereoscopic anaglyphs, each greatly enhanced by the real time interaction of volume data. The techniques and applications discussed in this thesis may prove to be invaluable tools in fields such as medical and molecular imaging, flow and scientific visualization, engineering drawing and many others

GPU ray casting

For many applications, such as walk-throughs or terrain visualization, drawing geometric primitives is the most efficient and effective way to represent the data. In contrast, other applications require the visualization of data that is inherently volumetric. For example, in biomedical imaging, it might be necessary to visualize 3D datasets obtained from CT or MRI scanners as a meaningful 2D image, in a process called volume rendering. As a result of the popularity and usefulness of volume data, a broad class of volume rendering techniques has emerged. Ray casting is one of these techniques. It allows for high quality volume rendering, but is a computationally expensive technique which, with current technology, lacks interactivity when visualizing large datasets, if processed on the CPU. The advent of efficient GPUs, available on almost every modern workstations, combined with their high degree of programmability opens up a wide field of new applications for the graphics cards. Ray casting is among these applications, exhibiting an intrinsic parallelism, in the form of completely independent light rays, which allows to take advantage of the massively parallel architecture of the GPU. This paper describes the implementation and analysis of a set of shaders which allow interactive volume rendering on the GPU by resorting to ray casting techniques

Real-time volume rendering and tractography visualization on the web

In the field of computer graphics, Volume Rendering techniques allow the visualization of 3D datasets, and specifically, Volume Ray-Casting renders images from volumetric datasets, typically used in some scientific areas, such as medical imaging -- This article aims to describe the development of a combined visualization of tractography and volume rendering of brain T1 MRI images in an integrated way -- An innovative web viewer for interactive visualization of neuro-imaging data has been developed based on WebGL -- This recently developed standard enables the clients to use the web viewer on a wide range of devices, with the only requirement of a compliant web-browser -- As the majority of the rendering tasks take place in the client machine, the effect of bottlenecks and server overloading are minimized -- The web application presented is able to compete with desktop tools, even supporting high graphical demands and facing challenges regarding performance and scalability -- The developed software modules are available as open source code and include MRI volume data and tractography generated by the Diffusion Toolkit, and connectivity data from the Connectome Mapping Toolkit -- Our contribution for the Volume Web Viewer implements early ray termination step according to the tractography depthmap, combining volume images and estimated white matter fibers -- Furthermore, the depthmap system extension can be used for visualization of other types of data, where geometric and volume elements are displayed simultaneousl

Interactive visualization tool for multi-channel confocal microscopy data in neurobiology research

Journal ArticleConfocal microscopy is widely used in neurobiology for studying the three-dimensional structure of the nervous system. Confocal image data are often multi-channel, with each channel resulting from a different fluorescent dye or fluorescent protein; one channel may have dense data, while another has sparse; and there are often structures at several spatial scales: subneuronal domains, neurons, and large groups of neurons (brain regions). Even qualitative analysis can therefore require visualization using techniques and parameters fine-tuned to a particular dataset. Despite the plethora of volume rendering techniques that have been available for many years, the techniques standardly used in neurobiological research are somewhat rudimentary, such as looking at image slices or maximal intensity projections. Thus there is a real demand from neurobiologists, and biologists in general, for a flexible visualization tool that allows interactive visualization of multi-channel confocal data, with rapid fine-tuning of parameters to reveal the three dimensional relationships of structures of interest. Together with neurobiologists, we have designed such a tool, choosing visualization methods to suit the characteristics of confocal data and a typical biologist's workflow. We use interactive volume rendering with intuitive settings for multidimensional transfer functions, multiple render modes and multi-views for multi-channel volume data, and embedding of polygon data into volume data for rendering and editing. As an example, we apply this tool to visualize confocal microscopy datasets of the developing zebrafish visual system

Brain explorer for connectomic analysis

Visualization plays a vital role in the analysis of multimodal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional, and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. Two types of non-spatial information are represented: (1) time series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image-based phenotypic biomarkers for brain diseases

Volume Ray casting with peak finding and differential sampling

Journal ArticleDirect volume rendering and isosurfacing are ubiquitous rendering techniques in scientific visualization, commonly employed in imaging 3D data from simulation and scan sources. Conventionally, these methods have been treated as separate modalities, necessitating different sampling strategies and rendering algorithms. In reality, an isosurface is a special case of a transfer function, namely a Dirac impulse at a given isovalue. However, artifact-free rendering of discrete isosurfaces in a volume rendering framework is an elusive goal, requiring either infinite sampling or smoothing of the transfer function. While preintegration approaches solve the most obvious deficiencies in handling sharp transfer functions, artifacts can still result, limiting classification. In this paper, we introduce a method for rendering such features by explicitly solving for isovalues within the volume rendering integral. In addition, we present a sampling strategy inspired by ray differentials that automatically matches the frequency of the image plane, resulting in fewer artifacts near the eye and better overall performance. These techniques exhibit clear advantages over standard uniform ray casting with and without preintegration, and allow for high-quality interactive volume rendering with sharp C0 transfer functions