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2011
Radiologists from all application areas are trained to read slice-based visualizations of 3D medical image data. Despite the numerous examples of sophisticated three-dimensional renderings, especially all variants of direct volume rendering, such methods are often considered not very useful by radiologists who prefer slice-based visualization. Just recently there have been attempts to bridge this gap between 2D and 3D renderings. These attempts include specialized techniques for volume picking that result in repositioning slices. In this paper, we present a new volume picking technique that, in contrast to previous work, does not require pre-segmented data or metadata. The positions picked by our method are solely based on the data itself, the transfer function and, most importantly, on the way the volumetric rendering is perceived by viewers. To demonstrate the usefulness of the proposed method we apply it for automatically repositioning slices in an abdominal MRI scan, a data set ...
2011
Radiologists from all application areas are trained to read slice-based visualizations of 3D medical image data. Despite the numerous examples of sophisticated threedimensional renderings, especially all variants of direct volume rendering, such methods are often considered not very useful by radiologists who prefer slice-based visualization. Just recently there have been attempts to bridge this gap between 2D and 3D renderings. These attempts include specialized techniques for volume picking that result in repositioning slices. In this paper, we present a new volume picking technique that, in contrast to previous work, does not require pre-segmented data or metadata. The positions picked by our method are solely based on the data itself, the transfer function and, most importantly, on the way the volumetric rendering is perceived by viewers. To demonstrate the usefulness of the proposed method we apply it for automatically repositioning slices in an abdominal MRI scan, a data set from a flow simulation and a number of other volumetric scalar fields. Furthermore we discuss how the method can be implemented in combination with various different volumetric rendering techniques.
2006
The technique of volume rendering can be a powerful tool when visualizing 3D medical data sets. Its characteristic of capturing 3D internal structures within a 2D rendered image makes it attractive in the analysis. However, the applications that implement this technique fail to reach out to most of the supposed end-users at the clinics and radiology departments of today. This is primarily due to problems centered on the design of the Transfer Function (TF), the tool that makes tissues visually appear in the rendered image. The interaction with the TF is too complex for a supposed end-user and its capability of separating tissues is often insufficient. This thesis presents methods for detecting the regions in the image volume where tissues are contained. The tissues that are of interest can furthermore be identified among these regions. This processing and classification is possible thanks to the use of a priori knowledge, i.e. what is known about the data set and its domain in advan...
Joint Eurographics - IEEE TCVG Symposium on Visualization, 2006
Slice-based visualizations of CT and MRI data are frequently used for diagnosis, intervention planning and intra- operative navigation since they allow a precise analysis and localization. We present new techniques to enhance the visualization of cross sectional medical image data. Our work is focussed on intervention planning and intra- operative navigation. We address the following problems of slice-based visualization in
Proceeding Visualization '91, 1991
Interactive direct visualization of 30 data requires fast update rates and the ability to extract regions of interest from the surrounding data. Parallel volume rendering yields rates that make interactive control of image viewing possible for the first time. We have achieved rates as high as I5 frames per second by trading some function for speed, while volume rendering with a full complement of ramp clussificution capabilities is performed at 1.4 frames per second. These speeds have made the combination of region selection with volume rendering practical for the first time. Semuniic driven selection, rather than geomciric clipping, hus proven to be a natural means of interacting with 30 data. Internal organs in medical data or other regions of interest can be built from preprocessed region primitives. We have applied the resulting combined system to red 3 0 medical data with encouraging results. The ideas presented ure not just limited to our platform. but can be generalized to include most parallel architectures. We present lessons learned from writing fust volume renderers and from applying image processing technique., to viewing volumetric data.
Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology, 2008
Volumetric imaging (computed tomography and magnetic resonance imaging) provides increased diagnostic detail but is associated with the problem of navigation through large amounts of data. In an attempt to overcome this problem, a novel 3D navigation tool has been designed and developed that is based on an alternative input device. A 3D mouse allows for simultaneous definition of position and orientation of orthogonal or oblique multiplanar reformatted images or slabs, which are presented within a virtual 3D scene together with the volume-rendered data set and additionally as 2D images. Slabs are visualized with maximum intensity projection, average intensity projection, or standard volume rendering technique. A prototype has been implemented based on PC technology that has been tested by several radiologists. It has shown to be easily understandable and usable after a very short learning phase. Our solution may help to fully exploit the diagnostic potential of volumetric imaging by...
2018
The proposed device offers virtual slicing of medical images with realistic spatial operation, boosts intuition and interaction, and enables users to view an anatomical target from any angle. The user holds the two handles of a monitor on each side and sweeps it through a virtual human body to view from the desired angle. The monitor is mounted on an encoded counter-balanced arm, which is movable with minimum effort through the human body volume. The encoders trace the position of the monitor which is used to compute the cross-sectional view. Our system generated cross-sectional views as the user moved the monitor within the defined workspace. The computed slices then are visualized on the Graphical User Interface. This device could enhance current digital education and radiology reading techniques by providing a practical and engaging tool to visualize the hidden features of a human body. Widespread adoption of 3D visualization techniques to observe medical images such as MRI scans...
Proceedings. XV Brazilian Symposium on Computer Graphics and Image Processing, 2002
With the evolution of medical image acquisition techniques, the capacity and fidelity of image-based diagnosis were extended. The current trend is to acquire information using multiple sources to help medical diagnosis, but the integration of the multivariate data into a single 3-D representation is non-trivial. Techniques for the visualization of multimodal volume data have been developed with the goal of finding suitable strategies to integrate characteristics of multiple data sets into a single visual representation. Likewise, several techniques are dedicated to the exploration of different ways of incorporating seeing-through capabilities into volume rendering techniques. This paper presents a new approach to visualize inner structures in multimodal volume data, which is based in the utilization of cutting tools.
1993
fast display method for volumetric data sets is presented, which involves a slice-f based method for extracting the potentially visible voxels to represent the visible sur aces. For a given viewing direction, the number of visible voxels can be trimmed a further by culling most of the voxels not visible from that direction. The entire 3D rray of voxels is also present for invasive operations and direct access to interior i structures. This approach has been integrated on a low-cost graphic engine as an nteractive system for craniofacial surgical planning that is currently in clinical use.
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.
2006
Abstract In medical imaging, many applications require visualization and/or analysis of three-dimensional (3D) objects (eg organs). At same time, object definition often requires considerable user assistance. In this process, objects are usually defined in an iterative way and their visualization during the process is very important to guide the user's actions for the next iteration. The usual procedure provides slice visualization during object definition (segmentation) and 3D visualization afterward.
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.
Biological and Medical Physics, Biomedical Engineering, 2011
Computers in Biology and Medicine, 2007
Volume data cutting plays a crucial part in medical image probing, computer assisted diagnosis, virtual surgery, etc. Based on hardwareaccelerated texture-based volume rendering algorithm, the paper proposes a method for volume cutting. With Boolean operations, the method is extended to multi-object clipping and can meet the needs of more complicated clipping applications. Due to hardware acceleration, proposed algorithms achieve interactive display rate and can be used in volume cutting applications such as surgery simulation and so on.
2005
Direct volume rendering (DVR) provides medical users with insight into datasets by creating a 3-D representation from a set of 2-D image slices (such as CT or MRI). This visualisation technique has been used to aid various medical diagnostic and therapy planning tasks. Volume rendering has recently become faster and more affordable with the advent of 3-D texture-mapping on commodity graphics hardware. Current implementations of the DVR algorithm on such hardware allow users to classify sample points (known as "voxels") using 2-D transfer functions (functions based on sample intensity and sample intensity gradient magnitude). However, such 2-D transfer functions inherently ignore spatial information. We present a novel modification to 3-D texture-based volume rendering allowing users to classify fuzzy-segmented, overlapping regions with independent 2-D transfer functions. This modification improves direct volume rendering by allowing for more sophisticated classification using spatial information.
2009
Segmentation of medical volume data sets (i.e., partitioning images into a set of disjoint regions representing different semantic objects) is an important research topic due to its large number of potential clinical applications. In order to get accepted by physicians and radiologists a generic, interactive 3D segmentation algorithm has to be simple-to-use, accurate, and show immediate feedback to the user. In this work we present a novel 3D segmentation paradigm that effectively combines interaction, segmentation and volumetric visualization in a single framework integrated on a modern graphics processing unit (GPU). This is an example of the fruitful combination of computer graphics and computer vision, a field nowadays called visual computing. Direct interaction with a large volumetric data set using 2D and 3D painting elements is combined with a segmentation algorithm formulated as a convex energy minimization. This globally optimal segmentation result and its evolution over ti...
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 2002: Visualization, Image-Guided Procedures, and Display, 2002
In this paper we present a method for interactive analysis of non-segmented medical volume data. We discuss both, different rendering methods for visualization, and different possibilities for interaction in relation to segmentation results. Furthermore, the adaptive region growing approach is applied to both, segmentation of a structure of interest, as well as generation of transfer function for volume rendering of the same structure. The adaptive region growing method is based on the statistical evaluation of 3D-neighbourhood. The method is used for determination of a homogeneity criterion for the structure of interest. Subsequently this criterion is used for segmenting of data and for generating of an initial transfer function for volume rendering. We utilize this for displaying a hybrid 3D-visualization of the segmented structure and the specific gray-value interval of original data. Based on this rendering we discuss possibilities for userguided validation of segmentation results, based on the variation of several rendering parameters. Recent developments in image modalities such as multislice spiral CT [1] have led to a substantial increase in resolution in slice direction, but it has also led to data sets of 300 slices or more. In practical application, the large number of slices per study will require fast, versatile and efficient methods for reducing the data for information extraction. Interactive 3D volume rendering, together with an integrated analysis, can be a practicable solution to support the assessment of slice data in a reasonable amount of time. In this paper we present a method that is related to our former work in the field of interactive exploration of medical data described in . Research into 3-d analysis is also supported by the fact that such high spatial resolution will make it possible to exploit 3-d coherence information that is inherent in the data. The amount of data to be processed (200 Megabyte or more) requires large capacities in terms of memory and speed for computation in reasonable time. On the other hand, the structure of interest (tumors, lesions, arterial structures, etc.) occupies a percentage of the voxels that is often much below 10% of all voxels. For analysis of such pathological structures, we developed a method that reduces rendering and analysis on a neighborhood of the structure of interest.
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.
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.
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