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        "abstract": "Real-time volume data acquisition poses substantial challenges for the traditional visualization pipeline where data enhancement is typically seen as a pre-processing step. In the case of 4D ultrasound data, for instance, costly processing operations to reduce noise and to remove artefacts need to be executed for every frame. To enable the use of high-quality filtering operations in such scenarios, we propose an output-sensitive approach to the visualization of streaming volume data. Our method evaluates the potential contribution of all voxels to the final image, allowing us to skip expensive processing operations that have little or no effect on the visualization. As filtering operations modify the data values which may affect the visibility, our main contribution is a fast scheme to predict their maximum effect on the final image. Our approach prioritizes filtering of voxels with high contribution to the final visualization based on a maximal permissible error per pixel. With zero permissible error, the optimized filtering will yield a result that is identical to filtering of the entire volume. We provide a thorough technical evaluation of the approach and demonstrate it on several typical scenarios that require on-the-fly processing.",
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        "title": "Perceptually Uniform Motion Space",
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        "abstract": "Flow data is often visualized by animated particles inserted into a ?ow ?eld. The velocity of a particle on the screen is typically linearly scaled by the velocities in the data. However, the perception of velocity magnitude in animated particles is not necessarily linear. We present a study on how different parameters affect relative motion perception. We have investigated the impact of four parameters. The parameters consist of speed multiplier, direction, contrast type and the global velocity scale. In addition, we investigated if multiple motion cues, and point distribution, affect the speed estimation. Several studies were executed to investigate the impact of each parameter. In the initial results, we noticed trends in scale and multiplier. Using the trends for the signi?cant parameters, we designed a compensation model, which adjusts the particle speed to compensate for the effect of the parameters. We then performed a second study to investigate the performance of the compensation model. From the second study we detected a constant estimation error, which we adjusted for in the last study. In addition, we connect our work to established theories in psychophysics by comparing our model to a model based on Stevens’ Power Law.",
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        "title": "The Ultrasound Visualization Pipeline",
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        "abstract": "Radiology is one of the main tools in modern medicine. A numerous set of deceases, ailments and treatments utilize accurate images of the patient. Ultrasound is one of the most frequently used imaging modality in medicine. The high spatial resolution, its interactive nature and non-invasiveness makes it the first choice in many examinations. Image interpretation is one of ultrasound’s main challenges. Much training is required to obtain a confident skill level in ultrasound-based diagnostics. State-of-the-art graphics techniques is needed to provide meaningful visualizations of ultrasound in real-time. In this paper we present the process-pipeline for ultrasound visualization, including an overview of the tasks performed in the specific steps. To provide an insight into the trends of ultrasound visualization research, we have selected a set of significant publications and divided them into a technique-based taxonomy covering the topics pre-processing, segmentation, registration, rendering and augmented reality. For the different technique types we discuss the difference between ultrasound-based techniques and techniques for other modalities.",
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        "title": "Visibility-Driven Processing of Streaming Volume Data",
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        "abstract": "In real-time volume data acquisition, such as 4D ultrasound, the raw data is challenging to visualize directly\nwithout additional processing. Noise removal and feature detection are common operations, but many methods\nare too costly to compute over the whole volume when dealing with live streamed data. In this paper, we propose\na visibility-driven processing scheme for handling costly on-the-fly processing of volumetric data in real-time. In contrast to the traditional visualization pipeline, our scheme utilizes a fast computation of the potentially visible subset of voxels which significantly reduces the amount of data required to process. As filtering operations modify the data values which may affect their visibility, our method for visibility-mask generation ensures that the set of elements deemed visible does not change after processing. Our approach also exploits the visibility information for the storage of intermediate values when multiple operations are performed in sequence, and can therefore significantly reduce the memory overhead of longer filter pipelines. We provide a thorough technical evaluation of the approach and demonstrate it on several typical scenarios where on-the-fly processing is required.",
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