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Start date: 2010 × Author: Kaufman, Arie ×

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    Multi-dimensional Reduction and Transfer Function Design using Parallel Coordinates
    (The Eurographics Association, 2010) Zhao, Xin; Kaufman, Arie; Ruediger Westermann and Gordon Kindlmann
    Multi-dimensional transfer functions are widely used to provide appropriate data classification for direct volume rendering. Nevertheless, the design of a multi-dimensional transfer function is a complicated task. In this paper, we propose to use parallel coordinates, a powerful tool to visualize high-dimensional geometry and analyze multivariate data, for multi-dimensional transfer function design. This approach has two major advantages: (1) Combining the information of spatial space (voxel position) and parameter space; (2) Selecting appropriate highdimensional parameters to obtain sophisticated data classification. Although parallel coordinates offers simple interface for the user to design the high-dimensional transfer function, some extra work such as sorting the coordinates is inevitable. Therefore, we use a local linear embedding technique for dimension reduction to reduce the burdensome calculations in the high dimensional parameter space and to represent the transfer function concisely. With the aid of parallel coordinates, we propose some novel high-dimensional transfer function widgets for better visualization results. We demonstrate the capability of our parallel coordinates based transfer function (PCbTF) design method for direct volume rendering using CT and MRI datasets.
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    Prostate Cancer Visualization from MR Imagery and MR Spectroscopy
    (The Eurographics Association and Blackwell Publishing Ltd., 2011) Marino, Joseph; Kaufman, Arie; H. Hauser, H. Pfister, and J. J. van Wijk
    Prostate cancer is one of the most prevalent cancers among males, and the use of magnetic resonance imaging (MRI) has been suggested for its detection. A framework is presented for scoring and visualizing various MR data in an efficient and intuitive manner. A classification method is introduced where a cumulative score volume is created which takes into account each of three acquisition types. This score volume is integrated into a volume rendering framework which allows the user to view the prostate gland, the multi-modal score values, and the sur- rounding anatomy. A visibility persistence mode is introduced to automatically avoid full occlusion of a selected score and indicate overlaps. The use of GPU-accelerated multi-modal single-pass ray casting provides an inter- active experience. User driven importance rendering allows the user to gain insight into the data and can assist in localization of the disease and treatment planning. We evaluate our results against pathology and radiologists' determinations.
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    Importance Driven Automatic Color Design for Direct Volume Rendering
    (The Eurographics Association and Blackwell Publishing Ltd., 2012) Wang, Lei; Kaufman, Arie; S. Bruckner, S. Miksch, and H. Pfister
    This paper introduces an automatic color design method that is driven by an importance function of the objects within a volumetric dataset. Our method allows the user to intuitively modify the object classification and the importance distribution function in the 2D rendered image. It automatically computes the transfer function, especially the color distribution, to convey the importance of the objects. In our approach, the importance of an object is represented as the attentiveness of a color. In addition, we preserve the color harmony in the rendered image in order to provide a visually pleasing result. In this paper, we propose a set of computational measurements to compute the color attentiveness and color harmony. Our color assignment algorithm supports arbitrary-dimensional transfer functions and obtains interactive frame rates. Our method involves three color spaces, namely Coloroid system, CIE LChuv, and Adobe RGB color space. It calculates the color attentiveness in CIE LChuv space, and the color harmony in Coloroid system. It, then, assigns the transfer function in a dual space of Adobe RGB space and renders the resulting image in Adobe RGB space. We conducted a detailed user study, which proves that our method successfully conveys the importance distributions. Our contribution in this paper is not only our importance driven approach, but also our computational measurements and our color assignment algorithm.
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    A Survey of Visualization and Analysis in High-Resolution Connectomics
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Beyer, Johanna; Troidl, Jakob; Boorboor, Saeed; Hadwiger, Markus; Kaufman, Arie; Pfister, Hanspeter; Bruckner, Stefan; Turkay, Cagatay; Vrotsou, Katerina
    The field of connectomics aims to reconstruct the wiring diagram of neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the neuronal connectivity, however, relies on many individual steps, starting from high-resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state-of-the-art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open-source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.