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2011
Volumetric visualization has many practical applications, particularly in medical imaging. Usability of volumetric visualization algorithms depends on available means to select areas of interest in volumetric data that are to be visualized. A simple and sucient method is a onedimensional transfer function that assigns colours to intensity values of the data. A drawback of this method is that it produces visual artefacts in specic cases. In this paper we propose a volumetric visualization method that overcomes this drawback by using ltration with volumetric ray casting algorithm. Our method enables users to use simple transfer functions with signicant visual artefacts reduction.
This paper presents Direct Volume Rendering (DVR) improvement strategies, which provide new opportunities for scientific and medical visualization which are not available in due measure in analogues: 1) multi-volume rendering in a single space of up to 3 volumetric datasets determined in different coordinate systems and having sizes as big as up to 512x512x512 16-bit values; 2) performing the above process in real time on a middle class GPU, e. g. nVidia GeForce GTS 250 512 M B; 3) a custom bounding mesh for more accurate selection of the desired region in addition to the clipping bounding box; 4) simultaneous usage of a number of visualization techniques including the shaded Direct Volume Rendering via the 1D-or 2D-transfer functions, multiple semi-transparent discrete iso-surfaces visualization, M IP, and M IDA. The paper discusses how the new properties affect the implementation of the DVR. In the DVR implementation we use such optimization strategies as the early ray termination and the empty space skipping. The clipping ability is also used as the empty space skipping approach to the rendering performance improvement. We use the random ray start position generation and the further frame accumulation in order to reduce the rendering artifacts. The rendering quality can be also improved by the onthe-fly tri-cubic filtering during the rendering process. Our framework supports 4 different stereoscopic visualization modes. Finally we outline the visualization performance in terms of the frame rates for different visualization techniques on different graphic cards.
International Journal of Innovative Technology and Exploring Engineering, 2020
Volume Rendering is the way to achieve 3D visualization. Volume Rendering is used for visualization of 2D projections of 3D data. In volume rendering techniques, direct volume rendering techniques (DVR) can be divided into image order and object order. Image order technique can be achieved by ray-casting algorithm. Ray-casting algorithm is used for raysurface interaction tests to solve problems in computer graphics like collision detection and hidden surface removal. In DVR, the ray is pushed through the object and 3D scalar field of interest is sampled along the ray inside the object. Over the years, different approaches towards this algorithm took place. This paper represents the review and analysis of different approaches of raycasting algorithm
Journal of Digital Imaging, 2010
With the increasing availability of high-resolution isotropic three-or four-dimensional medical datasets from sources such as magnetic resonance imaging, computed tomography, and ultrasound, volumetric image visualization techniques have increased in importance. Over the past two decades, a number of new algorithms and improvements have been developed for practical clinical image display. More recently, further efficiencies have been attained by designing and implementing volumerendering algorithms on graphics processing units (GPUs). In this paper, we review volumetric image visualization pipelines, algorithms, and medical applications. We also illustrate our algorithm implementation and evaluation results, and address the advantages and drawbacks of each algorithm in terms of image quality and efficiency. Within the outlined literature review, we have integrated our research results relating to new visualization, classification, enhancement, and multimodal data dynamic rendering. Finally, we illustrate issues related to modern GPU working pipelines, and their applications in volume visualization domain.
International Journal of Online and Biomedical Engineering (iJOE)
One of the most valuable medical imaging visualizations or computer-aided diagnosis is Volume rendering (VR). This survey’s objective is reviewing and comparing between several methods and techniques of VR, for a better and more comprehensive reading and learning of both pros and cons of each method, and their use cases.
Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2009
We describe and compare two volume visualization methods for Optical Coherence Tomography (OCT) retinal data sets. One of these methods is CPU-slicing, which is previously reported and used in our visualization engine. The other is GPU-ray casting. Several metrics including image quality, performance, hardware limitations and perception are used to grade the abilities of each method. We also discuss how to combine these methods to make a scalable volume visualization system that supports advanced lighting and dynamic volumetric shadowing techniques on a broad range of hardware. The feasibility of each visualization method for clinical application as well as potential further improvements are discussed.
IEEE Transactions on Visualization and Computer Graphics, 2000
Visualization of volumetric data faces the difficult task of finding effective parameters for the transfer functions. Those parameters can determine the effectiveness and accuracy of the visualization. Frequently, volumetric data includes multiple structures and features that need to be differentiated. However, if those features have the same intensity and gradient values, existing transfer functions are limited at effectively illustrating those similar features with different rendering properties. We introduce texture-based transfer functions for direct volume rendering. In our approach, the voxel's resulting opacity and color are based on local textural properties rather than individual intensity values. For example, if the intensity values of the vessels are similar to those on the boundary of the lungs, our texture-based transfer function will analyze the textural properties in those regions and color them differently even though they have the same intensity values in the volume. The use of texture-based transfer functions has several benefits. First, structures and features with the same intensity and gradient values can be automatically visualized with different rendering properties. Second, segmentation or prior knowledge of the specific features within the volume is not required for classifying these features differently. Third, textural metrics can be combined and/or maximized to capture and better differentiate similar structures. We demonstrate our texture-based transfer function for direct volume rendering with synthetic and real-world medical data to show the strength of our technique.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011
The visualization of images with a large dynamic range is a difficult task and this is especially the case for gray-level images. In radiology departments, this will force radiologists to review medical images several times, since the images need to be visualized with several different contrast windows (transfer functions) in order for the full content of each image to be seen. Previously suggested methods for handling this situation include various approaches using histogram equalization and other methods for processing the image data. However, none of these utilize the underlying human anatomy in the images to control the visualization and the fact that different transfer functions are often only relevant for disjoint anatomical regions. In this paper, we propose a method for using model-based local transfer functions. It allows the reviewing radiologist to apply multiple transfer functions simultaneously to a medical image volume. This provides the radiologist with a tool for making the review process more efficient, by allowing him/her to review more of the information in a medical image volume with a single visualization. The transfer functions are automatically assigned to different anatomically relevant regions, based upon a model registered to the volume to be visualized. The transfer functions can be either pre-defined or interactively changed by the radiologist during the review process. All of this is achieved without adding any unfamiliar aspects to the radiologist's normal work process, when reviewing medical image volumes.
Biological and Medical Physics, Biomedical Engineering, 2011
Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures, 2001
There are various 3D visualization methods such as volume rendering and surface renderingS The volume rendering (VR) is a useful tool to visualize 3D medical images. However, a requirement of large computation amount makes it difficult for the VR to be used in real-time medical applications. In order to overcome the large computation amount of the VR, we have developed a progressive VR (PVR) method that can perform the low-resolution VR for fast and intuitive processing and use the depth information from the low-resolution VR to generate the full-resolution VR image with a reduced computation time. The developed algorithm can be applicable to the real-time applications of the YR. Le., the low-resolution VR is performed interactively according to change of view direction, and the full-resolution VR is performed once we fix the view direction In this paper its computation complexity and image quality are analyzed Also an extension of its progressive refinement is introduced.
2013
Optical parameter assignment via Transfer Functions (TF) is the sole interactive part in medical visualization via volume rendering. Being an interactive element of the rendering pipeline, TF specification has very important effects on the quality of volume-rendered medical images. However, TF specification should be supported by informative search spaces, interactive data exploration tools and intuitive user interfaces. Due to the trade-off between user control and TF domain complexity, integrating different features into the TF without losing user interaction is a challenging task since both are needed to fulfill the expectations of a physician. By addressing this problem, we introduce a semi-automatic method for initial generation of TFs. The proposed method extends the concept of recently introduced Volume Histogram Stack (VHS), which is a new domain constructed by aligning the histograms of the image slices of a CT and/or MR series. In this study, the VHS concept is extended by...
Mankind is favoured with great amount of information through Information Technology. Consequently, such enormous information requires proper filtering for necessary essentials. With the aid of visualization, medical communities now record many breakthroughs in their diagnosis and radiotherapy treatments. The computer aided visualization of information has been identified to be a very useful and accurate tool for translating abstract data into images which can be inspected and analyzed more easily. DVR is a visualization technique that aims to convey an entire 3D data set in a 2D image without intermediate representation. This paper reviews a number of previous works on volumetric image visualization, describes direct volume rendering in depth and likewise crystallizes challenges, advantages and limitations of some of the techniques in order to assist further research in the medical volume visualization and perhaps scientific visualization at large.
Medical Imaging 2008: Visualization, Image-guided Procedures, and Modeling, 2008
The two major volume visualization methods used in biomedical applications are Maximum Intensity Projection (MIP) and Volume Rendering (VR), both of which involve the process of creating sets of 2D projections from 3D images. We have developed a new method for very fast, high-quality volume visualization of 3D biomedical images, based on the fact that the inverse of this process (transforming 2D projections into a 3D image) is essentially equivalent to tomographic image reconstruction. This new method uses the 2D projections acquired by the scanner, thereby obviating the need for the two computationally expensive steps currently required in the complete process of biomedical visualization, that is, (i) reconstructing the 3D image from 2D projection data, and (ii) computing the set of 2D projections from the reconstructed 3D image As well as improvements in computation speed, this method also results in improvements in visualization quality, and in the case of x-ray CT we can exploit this quality improvement to reduce radiation dosage. In this paper, demonstrate the benefits of developing biomedical visualization techniques by directly processing the sensor data acquired by body scanners, rather than by processing the image data reconstructed from the sensor data. We show results of using this approach for volume visualization for tomographic modalities, like x-ray CT, and as well as for MRI.
Medical Imaging 2008: Visualization, Image-guided Procedures, and Modeling, 2008
The two major volume visualization methods used in biomedical applications are Maximum Intensity Projection (MIP) and Volume Rendering (VR), both of which involve the process of creating sets of 2D projections from 3D images. We have developed a new method for very fast, high-quality volume visualization of 3D biomedical images, based on the fact that the inverse of this process (transforming 2D projections into a 3D image) is essentially equivalent to tomographic image reconstruction. This new method uses the 2D projections acquired by the scanner, thereby obviating the need for the two computationally expensive steps currently required in the complete process of biomedical visualization, that is, (i) reconstructing the 3D image from 2D projection data, and (ii) computing the set of 2D projections from the reconstructed 3D image As well as improvements in computation speed, this method also results in improvements in visualization quality, and in the case of x-ray CT we can exploit this quality improvement to reduce radiation dosage. In this paper, demonstrate the benefits of developing biomedical visualization techniques by directly processing the sensor data acquired by body scanners, rather than by processing the image data reconstructed from the sensor data. We show results of using this approach for volume visualization for tomographic modalities, like x-ray CT, and as well as for MRI.
Lecture Notes in Computer Science, 2007
Volumetric data rendering has become an important tool in various medical procedures as it allows the unbiased visualization of fine details of volumetric medical data (CT, MRI, fMRI). However, due to the large amount of computation involved, the rendering time increases dramatically as the size of the data set grows. This paper presents several acceleration techniques of volume rendering using general-purpose GPU. Some techniques enhance the rendering speed of software ray casting based on voxels' opacity information, while the others improve traditional hardware-accelerated object-order volume rendering. Remarkable speedups are observed using the proposed GPU-based algorithm from experiments on routine medical data sets.
Computer Graphics Forum, 2016
A central topic in scientific visualization is the transfer function (TF) for volume rendering. The TF serves a fundamental role in translating scalar and multivariate data into color and opacity to express and reveal the relevant features present in the data studied. Beyond this core functionality, TFs also serve as a tool for encoding and utilizing domain knowledge and as an expression for visual design of material appearances. TFs also enable interactive volumetric exploration of complex data. The purpose of this state‐of‐the‐art report (STAR) is to provide an overview of research into the various aspects of TFs, which lead to interpretation of the underlying data through the use of meaningful visual representations. The STAR classifies TF research into the following aspects: dimensionality, derived attributes, aggregated attributes, rendering aspects, automation, and user interfaces. The STAR concludes with some interesting research challenges that form the basis of an agenda fo...
Proceedings Visualization '98 (Cat. No.98CB36276), 1998
For high quality rendering of objects segmented from tomographic volume data the precise location of the boundaries of adjacent objects in subvoxel resolution is required. We describe a new method that determines the membership of a given sample point to an object by reclassifying the sample point using interpolation of the original intensity values and searching for the best fitting object in the neighbourhood. Using a ray-casting approach we then compute the surface location between successive sample points along the viewingray by interpolation or bisection. The accurate calculation of the object boundary enables a much more precise computation of the gray-level-gradient yielding the surface normal. Our new approach significantly improves the quality of reconstructed and shaded surfaces and reduces aliasing artifacts for animations and magnified views. We illustrate the results on different cases including the Visible-Human-Data, where we achieve nearly photo-realistic images.
International Congress Series, 2003
With modern CT scanners, radiologists are facing an ever increasing number of images not possible to review on a slice by slice basis. During the past years, volume rendering has developed to an interesting alternative for reading large medical data volumes. Due to the increasing computer power and the development of dedicated acceleration hardware, it can now be realized as a real-time system with standard personal computers at reasonable costs. However, the specification of transfer functions needed to visualize features of interest is still a difficult task [W. Schroeder, C. Bajaj, G. Kindlmann, H. Pfister, 2000. The Transfer Function Bake-Off. IEEE Visualization Conference]. A fast and simple technique for setting transfer functions is crucial for clinical routine work. We present a novel, interactive graphical user interface to deal with this problem.
Image and Vision Computing …
Spectral Computed Tomography (CT) is a new approach for medical diagnosis. Where traditional methods deliver a unique 3D volumetric dataset, spectral CT supplies multiple 3D volumetric datasets (energy bins). The novelty of this technique creates new opportunities and new challenges regarding visualization of the acquired data. In this paper we describe our approach and effort for providing high quality visualization of this new type of dataset. After a visual characterization of the data, we describe a set of image filtering algorithms we implemented and the results we obtained.
arXiv, 2022
Colorization Volume Visualization Stack of 2D MRI head scans (color) Stack of 2D MRI head scans (gray) Rendered Volume Figure 1: The proposed system comprises of colorization of grayscale medical images followed by direct volume rendering.
IEEE Transactions on Visualization and Computer Graphics, 2000
The growing sizes of volumetric data sets pose a great challenge for interactive visualization. In this paper, we present a feature-preserving data reduction and focus+context visualization method based on transfer function driven, continuous voxel repositioning and resampling techniques. Rendering reduced data can enhance interactivity. Focus+context visualization can show details of selected features in context on display devices with limited resolution. Our method utilizes the input transfer function to assign importance values to regularly partitioned regions of the volume data. According to user interaction, it can then magnify regions corresponding to the features of interest while compressing the rest by deforming the 3D mesh. The level of data reduction achieved is significant enough to improve overall efficiency. By using continuous deformation, our method avoids the need to smooth the transition between low and high-resolution regions as often required by multiresolution methods. Furthermore, it is particularly attractive for focus+context visualization of multiple features. We demonstrate the effectiveness and efficiency of our method with several volume data sets from medical applications and scientific simulations.
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