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MultiSciView: Multivariate Scientific X-ray Image Visual
Exploration with Cross-Data Space Views |
WenZhongb, WeiXua, KevinG.Yagera,GregoryS.Doerka,JianZhaoc,YunkeTiane,SungsooHaa,CongXieb,YuanZhongd,KlausMuellerb,Kerstin KleeseVan Dama |
aBrookhaven National Laboratory, Upton, New York, United States
bStony Brook University, Stony Brook, New York, United States
cFX Palo Alto Laboratory, Palo Alto, California, United States
dFacebook Inc., Menlo Park, California, United States
eMidea Emerging Technology Center, San Jose, California, United States |
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Abstract
X-ray images obtained from synchrotron
beamlines are large-scale, high-resolution and high-dynamic-range grayscale
data encoding multiple complex properties of the measured materials. They are
typically associated with a variety of metadata which increases their inherent
complexity. There is a wealth of information embedded in these data but so far
scientists lack modern exploration tools to unlock these hidden treasures. To
bridge this gap, we propose MultiSciView, a multivariate scientific x-ray image
visualization and exploration system for beamline-generated x-ray scattering
data. Our system is composed of three complementary and coordinated interactive
visualizations to enable a coordinated exploration across the images and their
associated attribute and feature spaces. The first visualization features a
multi-level scatterplot visualization dedicated for image exploration in
attribute, image, and pixel scales. The second visualization is a histogram-based
attribute cross filter by which users can extract desired subset patterns from
data. The third one is an attribute projection visualization designed for
capturing global attribute correlations. We demonstrate our framework by ways
of a case study involving a real-world material scattering dataset. We show
that our system can efficiently explore large-scale x-ray images, accurately
identify preferred image patterns, anomalous images and erroneous experimental
settings, and effectively advance the comprehension of material nanostructure
properties.
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Published: 01 January 1900
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Cite this article:
WenZhong, WeiXu, KevinG.Yager, GregoryS.Doerk, JianZhao, YunkeTiae, SungsooHa, CongXie, YuanZhong, KlausMueller, Kerstin KleeseVan Dam. MultiSciView: Multivariate Scientific X-ray Image Visual
Exploration with Cross-Data Space Views. Vis Inf, 2018, 2(1): 14-25.
URL:
http://www.zjujournals.com/vi/10.1016/j.visinf.2018.04.003 OR http://www.zjujournals.com/vi/Y2018/V2/I1/14
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MultiSciView: 从交叉数据空间视角进行X射线图像的多变量可视分析
背景:由同步加速器光束生成的X射线图像是大规模、高分辨率和高动态范围的灰度数据,是对被测材料多种复杂属性的一种编码。它们通常与各种元数据相关联,从而增加了其内在的复杂性。这些数据中蕴含了丰富的信息,但迄今为止,科学家们尚缺乏现代化探索工具来揭开隐藏在这些信息中的财富。
贡献:为填补这一鸿沟,本文提出了一种面向光束生成的X射线散射数据的多变量的X射线图像科学可视化和探索系统:MultiSciView。该系统由三种相互补充、协调一致的交互式可视化方式组成,以实现对X射线像图像及其关联属性和特征空间的协同探索。第一种方式是基于多级散点图的可视化,采用属性、图像和像素尺度对图像进行探究。第二种方式是基于直方图的属性交叉过滤器,用户可以从数据中提取所希望的子集模式。第三种方式是属性射影可视化,用于捕捉属性的整体相关性。
结果:
本文通过对一个真实材料散射数据集的案例来演示所提出的系统。结果表明,该系统可对大规模的X射线图像实施高效的探索,准确地检出想要的图像模式、异常图像和错误的实验设置,有效地提高对材料纳米结构性质的理解。
关键词:
X射线图像的科学可视化,
多级散点图,
交叉数据空间探索
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