Importance. Medical images are essential for modern medicine and an important research subject in visualization. However,medical experts are often not aware of the many advanced three-dimensional (3D) medical image vi...Importance. Medical images are essential for modern medicine and an important research subject in visualization. However,medical experts are often not aware of the many advanced three-dimensional (3D) medical image visualization techniques thatcould increase their capabilities in data analysis and assist the decision-making process for specific medical problems. Ourpaper provides a review of 3D visualization techniques for medical images, intending to bridge the gap between medicalexperts and visualization researchers. Highlights. Fundamental visualization techniques are revisited for various medicalimaging modalities, from computational tomography to diffusion tensor imaging, featuring techniques that enhance spatialperception, which is critical for medical practices. The state-of-the-art of medical visualization is reviewed based on aprocedure-oriented classification of medical problems for studies of individuals and populations. This paper summarizes freesoftware tools for different modalities of medical images designed for various purposes, including visualization, analysis, andsegmentation, and it provides respective Internet links. Conclusions. Visualization techniques are a useful tool for medicalexperts to tackle specific medical problems in their daily work. Our review provides a quick reference to such techniques giventhe medical problem and modalities of associated medical images. We summarize fundamental techniques and readily availablevisualization tools to help medical experts to better understand and utilize medical imaging data. This paper could contributeto the joint effort of the medical and visualization communities to advance precision medicine.展开更多
Dynamic Mode Decomposition(DMD)is a data-driven and model-free decomposition technique.It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data.Conceptually,DMD perfor...Dynamic Mode Decomposition(DMD)is a data-driven and model-free decomposition technique.It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data.Conceptually,DMD performs a low-dimensional spectral decomposition of the data into the following components:the modes,called DMD modes,encode the spatial contribution of the decomposition,whereas the DMD amplitudes specify their impact.Each associated eigenvalue,referred to as DMD eigenvalue,characterizes the frequency and growth rate of the DMD mode.In this paper,we demonstrate how the components of DMD can be utilized to obtain temporal and spatial information from time-dependent flow fields.We begin with the theoretical background of DMD and its application to unsteady flow.Next,we examine the conventional process with DMD mathematically and put it in relationship to the discrete Fourier transform.Our analysis shows that the current use of DMD components has several drawbacks.To resolve these problems we adjust the components and provide new and meaningful insights into the decomposition:we show that our improved components capture the spatio-temporal patterns of the flow better.Moreover,we remove redundancies in the decomposition and clarify the interplay between components,allowing users to understand the impact of components.These new representations,which respect the spatio-temporal character of DMD,enable two clustering methods that segment the flow into physically relevant sections and can therefore be used for the selection of DMD components.With a number of typical examples,we demonstrate that the combination of these techniques allows new insights with DMD for unsteady flow.展开更多
Dimensionality reduction is often used to project time series data from multidimensional to two-dimensional space to generate visual representations of the temporal evolution.In this context,we address the problem of ...Dimensionality reduction is often used to project time series data from multidimensional to two-dimensional space to generate visual representations of the temporal evolution.In this context,we address the problem of multidimensional time series visualization by presenting a new method to show and handle projection errors introduced by dimensionality reduction techniques on multidimensional temporal data.For visualization,subsequent time instances are rendered as dots that are connected by lines or curves to indicate the temporal dependencies.However,inevitable projection artifacts may lead to poor visualization quality and misinterpretation of the temporal information.Wrongly projected data points,inaccurate variations in the distances between projected time instances,and intersections of connecting lines could lead to wrong assumptions about the original data.We adapt local and global quality metrics to measure the visual quality along the projected time series,and we introduce a model to assess the projection error at intersecting lines.These serve as a basis for our new uncertainty visualization techniques that use different visual encodings and interactions to indicate,communicate,and work with the visualization uncertainty from projection errors and artifacts along the timeline of data points,their connections,and intersections.Our approach is agnostic to the projection method and works for linear and non-linear dimensionality reduction methods alike.展开更多
We present angle-uniform parallel coordinates,a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped alon...We present angle-uniform parallel coordinates,a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates plot.Despite being a common method for visualizing multidimensional data,parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable estimations.To address this issue,we introduce a transformation that bounds all points horizontally using an angleuniform mapping and shrinks them vertically in a structure-preserving fashion;polygonal lines become smooth curves and a symmetric representation of data correlations is achieved.We further propose a combined subsampling and density visualization approach to reduce visual clutter caused by overdrawing.Our method enables accurate visual pattern interpretation of data correlations,and its data-independent nature makes it applicable to all multidimensional datasets.The usefulness of our method is demonstrated using examples of synthetic and real-world datasets.展开更多
We investigate task performance and reading characteristics for scatterplots(Cartesian coordinates)and parallel coordinates.In a controlled eye-tracking study,we asked 24 participants to assess the relative distance o...We investigate task performance and reading characteristics for scatterplots(Cartesian coordinates)and parallel coordinates.In a controlled eye-tracking study,we asked 24 participants to assess the relative distance of points in multidimensional space,depending on the diagram type(parallel coordinates or a horizontal collection of scatterplots),the number of data dimensions(2,4,6,or 8),and the relative distance between points(15%,20%,or 25%).For a given reference point and two target points,we instructed participants to choose the target point that was closer to the reference point in multidimensional space.We present a visual scanning model that describes different strategies to solve this retrieval task for both diagram types,and propose corresponding hypotheses that we test using task completion time,accuracy,and gaze positions as dependent variables.Our results show that scatterplots outperform parallel coordinates significantly in 2 dimensions,however,the task was solved more quickly and more accurately with parallel coordinates in 8 dimensions.The eye-tracking data further shows significant differences between Cartesian and parallel coordinates,as well as between different numbers of dimensions.For parallel coordinates,there is a clear trend toward shorter fixations and longer saccades with increasing number of dimensions.Using an area-of-interest(AOI)based approach,we identify different reading strategies for each diagram type:For parallel coordinates,the participants’gaze frequently jumped back and forth between pairs of axes,while axes were rarely focused on when viewing Cartesian coordinates.We further found that participants’attention is biased:toward the center of the whole plot for parallel coordinates and skewed to the center/left side for Cartesian coordinates.We anticipate that these results may support the design of more effective visualizations for multidimensional data.展开更多
基金the Data for Better Health Project of Peking University-Master Kong and by NIH(R01 EB031872).
文摘Importance. Medical images are essential for modern medicine and an important research subject in visualization. However,medical experts are often not aware of the many advanced three-dimensional (3D) medical image visualization techniques thatcould increase their capabilities in data analysis and assist the decision-making process for specific medical problems. Ourpaper provides a review of 3D visualization techniques for medical images, intending to bridge the gap between medicalexperts and visualization researchers. Highlights. Fundamental visualization techniques are revisited for various medicalimaging modalities, from computational tomography to diffusion tensor imaging, featuring techniques that enhance spatialperception, which is critical for medical practices. The state-of-the-art of medical visualization is reviewed based on aprocedure-oriented classification of medical problems for studies of individuals and populations. This paper summarizes freesoftware tools for different modalities of medical images designed for various purposes, including visualization, analysis, andsegmentation, and it provides respective Internet links. Conclusions. Visualization techniques are a useful tool for medicalexperts to tackle specific medical problems in their daily work. Our review provides a quick reference to such techniques giventhe medical problem and modalities of associated medical images. We summarize fundamental techniques and readily availablevisualization tools to help medical experts to better understand and utilize medical imaging data. This paper could contributeto the joint effort of the medical and visualization communities to advance precision medicine.
文摘Dynamic Mode Decomposition(DMD)is a data-driven and model-free decomposition technique.It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data.Conceptually,DMD performs a low-dimensional spectral decomposition of the data into the following components:the modes,called DMD modes,encode the spatial contribution of the decomposition,whereas the DMD amplitudes specify their impact.Each associated eigenvalue,referred to as DMD eigenvalue,characterizes the frequency and growth rate of the DMD mode.In this paper,we demonstrate how the components of DMD can be utilized to obtain temporal and spatial information from time-dependent flow fields.We begin with the theoretical background of DMD and its application to unsteady flow.Next,we examine the conventional process with DMD mathematically and put it in relationship to the discrete Fourier transform.Our analysis shows that the current use of DMD components has several drawbacks.To resolve these problems we adjust the components and provide new and meaningful insights into the decomposition:we show that our improved components capture the spatio-temporal patterns of the flow better.Moreover,we remove redundancies in the decomposition and clarify the interplay between components,allowing users to understand the impact of components.These new representations,which respect the spatio-temporal character of DMD,enable two clustering methods that segment the flow into physically relevant sections and can therefore be used for the selection of DMD components.With a number of typical examples,we demonstrate that the combination of these techniques allows new insights with DMD for unsteady flow.
基金Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germany’s Excellence Strategy–EXC-2075–390740016.
文摘Dimensionality reduction is often used to project time series data from multidimensional to two-dimensional space to generate visual representations of the temporal evolution.In this context,we address the problem of multidimensional time series visualization by presenting a new method to show and handle projection errors introduced by dimensionality reduction techniques on multidimensional temporal data.For visualization,subsequent time instances are rendered as dots that are connected by lines or curves to indicate the temporal dependencies.However,inevitable projection artifacts may lead to poor visualization quality and misinterpretation of the temporal information.Wrongly projected data points,inaccurate variations in the distances between projected time instances,and intersections of connecting lines could lead to wrong assumptions about the original data.We adapt local and global quality metrics to measure the visual quality along the projected time series,and we introduce a model to assess the projection error at intersecting lines.These serve as a basis for our new uncertainty visualization techniques that use different visual encodings and interactions to indicate,communicate,and work with the visualization uncertainty from projection errors and artifacts along the timeline of data points,their connections,and intersections.Our approach is agnostic to the projection method and works for linear and non-linear dimensionality reduction methods alike.
基金support from the Data for Better Health Project of Peking University-Master Kong,YW from the National Natural Science Foundation of China(62132017)DW from the Deutsche Forschungsgemeinschaft(DFG)Project-ID 251654672-TRR 161.
文摘We present angle-uniform parallel coordinates,a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates plot.Despite being a common method for visualizing multidimensional data,parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable estimations.To address this issue,we introduce a transformation that bounds all points horizontally using an angleuniform mapping and shrinks them vertically in a structure-preserving fashion;polygonal lines become smooth curves and a symmetric representation of data correlations is achieved.We further propose a combined subsampling and density visualization approach to reduce visual clutter caused by overdrawing.Our method enables accurate visual pattern interpretation of data correlations,and its data-independent nature makes it applicable to all multidimensional datasets.The usefulness of our method is demonstrated using examples of synthetic and real-world datasets.
基金We would like to thank the Carl-Zeiss-Foundation(Carl-Zeiss-Stiftung)the German Research Foundation(DFG)for financial support within project B01 of SFB/Transregio 161.
文摘We investigate task performance and reading characteristics for scatterplots(Cartesian coordinates)and parallel coordinates.In a controlled eye-tracking study,we asked 24 participants to assess the relative distance of points in multidimensional space,depending on the diagram type(parallel coordinates or a horizontal collection of scatterplots),the number of data dimensions(2,4,6,or 8),and the relative distance between points(15%,20%,or 25%).For a given reference point and two target points,we instructed participants to choose the target point that was closer to the reference point in multidimensional space.We present a visual scanning model that describes different strategies to solve this retrieval task for both diagram types,and propose corresponding hypotheses that we test using task completion time,accuracy,and gaze positions as dependent variables.Our results show that scatterplots outperform parallel coordinates significantly in 2 dimensions,however,the task was solved more quickly and more accurately with parallel coordinates in 8 dimensions.The eye-tracking data further shows significant differences between Cartesian and parallel coordinates,as well as between different numbers of dimensions.For parallel coordinates,there is a clear trend toward shorter fixations and longer saccades with increasing number of dimensions.Using an area-of-interest(AOI)based approach,we identify different reading strategies for each diagram type:For parallel coordinates,the participants’gaze frequently jumped back and forth between pairs of axes,while axes were rarely focused on when viewing Cartesian coordinates.We further found that participants’attention is biased:toward the center of the whole plot for parallel coordinates and skewed to the center/left side for Cartesian coordinates.We anticipate that these results may support the design of more effective visualizations for multidimensional data.
