scholarly journals FOCAL REGION-BASED VOLUME RENDERING

2006 ◽  
Vol 20 (05) ◽  
pp. 665-677 ◽  
Author(s):  
JIANLONG ZHOU ◽  
ZHIYAN WANG ◽  
KLAUS D. TÖNNIES
Keyword(s):  
Volume Rendering ◽  
Contextual Information ◽  
Focal Region ◽  
Ray Casting ◽  
Volumetric Data ◽  
Data Set ◽  
Volume Of Interest ◽  
Bounded Volume ◽  
Volume Ray Casting ◽  
Main Components

In this paper, a new approach named focal region-based volume rendering for visualizing internal structures of volumetric data is presented. This approach presents volumetric information through integrating context information as the structure analysis of the data set with a lens-like focal region rendering to show more detailed information. This feature-based approach contains three main components: (i) A feature extraction model using 3D image processing techniques to explore the structure of objects to provide contextual information; (ii) An efficient ray-bounded volume ray casting rendering to provide the detailed information of the volume of interest in the focal region; (iii) The tools used to manipulate focal regions to make this approach more flexible. The approach provides a powerful framework for producing detailed information from volumetric data. Providing contextual information and focal region renditions at the same time has the advantages of easy to understand and comprehend volume information for the scientist. The interaction techniques provided in this approach make the focal region-based volume rendering more flexible and easy to use.

scholarly journals Effective volume rendering on mobile and standalone VR headsets by means of a hybrid method

2020 ◽  
Vol 15 (2) ◽  
pp. 3-12
Author(s):  
Balázs Tukora
Keyword(s):  
Virtual Reality ◽  
Volume Rendering ◽  
Texture Mapping ◽  
Volume Visualization ◽  
Frame Rate ◽  
Hybrid Technique ◽  
Ray Casting ◽  
Volumetric Data ◽  
3D Texture ◽  
Rendering Techniques

Abstract:Numerous volume rendering techniques are available to display 3D datasets on desktop computers and virtual reality devices. Recently the spreading of mobile and standalone virtual reality headsets has brought the need for volume visualization on these platforms too. However, the volume rendering techniques that show good performance in desktop environment underachieve on these devices, due to the special hardware conditions and visualization requirements. To speed up the volumetric rendering to an accessible level a hybrid technique is introduced, a mix of the ray casting and 3D texture mapping methods. This technique increases 2-4 times the frame rate of displaying volumetric data on mobile and standalone virtual reality headsets as compared to the original methods. The new technique was created primarily to display medical images but it is not limited only to this type of volumetric data.

scholarly journals Streaming Model Based Volume Ray Casting Implementation for Cell Broadband Engine

2009 ◽  
Vol 17 (1-2) ◽  
pp. 173-184 ◽  
Author(s):  
Jusub Kim ◽  
Joseph JaJa
Keyword(s):  
Parallel Implementation ◽  
Memory Bandwidth ◽  
Ray Casting ◽  
Volumetric Data ◽  
Computational Power ◽  
High Quality ◽  
Cell Broadband Engine ◽  
Model Based ◽  
Acceleration Techniques ◽  
Volume Ray Casting

Interactive high quality volume rendering is becoming increasingly more important as the amount of more complex volumetric data steadily grows. While a number of volumetric rendering techniques have been widely used, ray casting has been recognized as an effective approach for generating high quality visualization. However, for most users, the use of ray casting has been limited to datasets that are very small because of its high demands on computational power and memory bandwidth. However the recent introduction of the Cell Broadband Engine (Cell B.E.) processor, which consists of 9 heterogeneous cores designed to handle extremely demanding computations with large streams of data, provides an opportunity to put the ray casting into practical use. In this paper, we introduce an efficient parallel implementation of volume ray casting on the Cell B.E. The implementation is designed to take full advantage of the computational power and memory bandwidth of the Cell B.E. using an intricate orchestration of the ray casting computation on the available heterogeneous resources. Specifically, we introduce streaming model based schemes and techniques to efficiently implement acceleration techniques for ray casting on Cell B.E. In addition to ensuring effective SIMD utilization, our method provides two key benefits: there is no cost for empty space skipping and there is no memory bottleneck on moving volumetric data for processing. Our experimental results show that we can interactively render practical datasets on a single Cell B.E. processor.

scholarly journals A Comparison between GPU-based Volume Ray Casting Implementations: Fragment Shader, Compute Shader, OpenCL, and CUDA

2017 ◽  
Vol 20 (2) ◽  
Author(s):  
Francisco Sans ◽  
Rhadamés Carmona
Keyword(s):  
Computer Graphics ◽  
Volume Rendering ◽  
Oil And Gas ◽  
Performance Comparison ◽  
Ray Casting ◽  
Volume Ray Casting ◽  
Area Of Study ◽  
A Performance

Volume rendering is an important area of study in computer graphics, due to its application in areas such as medicine, physic simulations, oil and gas industries, and others. The main used method nowadays for volume rendering is ray casting. Nevertheless, there are a variety of parallel APIs that can be used to implement it. Thus, it is important to evaluate the performance of ray casting in diferent parallel APIs to help programmers in selecting one of them. In this paper, we present a performance comparison using OpenGL® with fragment shader, OpenGL® with compute shader, OpenCL, and CUDA.

scholarly journals GPU Volume Ray Casting of two Volumes within VTK

2012 ◽  
Author(s):  
Karl Krissian ◽  
Carlos Falcón-Torres
Keyword(s):  
Volume Rendering ◽  
Transfer Functions ◽  
Ray Casting ◽  
The Creation ◽  
Volume Ray Casting

We have modified the current VTK volume rendering on GPU to allow simultaneous rendering of two volumes, each of them with its own color and opacity transfer functions. These changes have led to the creation of two new C++ classes and several GLSL shaders. We explain the modifications made to the original classes and shaders and we discuss possible additional improvements. A C++ demo code shows how to use the new classes.

scholarly journals Segmentation Method for Magnetic Resonance-Guided High-Intensity Focused Ultrasound Therapy Planning

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
A. Vargas-Olivares ◽  
O. Navarro-Hinojosa ◽  
M. Maqueo-Vicencio ◽  
L. Curiel ◽  
M. Alencastre-Miranda ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
High Intensity ◽  
Focused Ultrasound ◽  
High Intensity Focused Ultrasound ◽  
Segmentation Algorithm ◽  
Focal Region ◽  
Therapy Planning ◽  
Mr Images ◽  
Monitoring Method ◽  
Data Set

High-intensity focused ultrasound (HIFU) is a minimally invasive therapy modality in which ultrasound beams are concentrated at a focal region, producing a rise of temperature and selective ablation within the focal volume and leaving surrounding tissues intact. HIFU has been proposed for the safe ablation of both malignant and benign tissues and as an agent for drug delivery. Magnetic resonance imaging (MRI) has been proposed as guidance and monitoring method for the therapy. The identification of regions of interest is a crucial procedure in HIFU therapy planning. This procedure is performed in the MR images. The purpose of the present research work is to implement a time-efficient and functional segmentation scheme, based on the watershed segmentation algorithm, for the MR images used for the HIFU therapy planning. The achievement of a segmentation process with functional results is feasible, but preliminary image processing steps are required in order to define the markers for the segmentation algorithm. Moreover, the segmentation scheme is applied in parallel to an MR image data set through the use of a thread pool, achieving a near real-time execution and making a contribution to solve the time-consuming problem of the HIFU therapy planning.

Sign in / Sign up

Export Citation Format

Share Document