Measuring the Effects of Scalar and Spherical Colormaps on Ensembles of DMRI Tubes

We report empirical study results on the color encoding of ensemble scalar and orientation to visualize diffusion magnetic resonance imaging (DMRI) tubes. The experiment tested six scalar colormaps for average fractional anisotropy (FA) tasks (grayscale, blackbody, diverging, isoluminant-rainbow, extended-blackbody, and coolwarm) and four three-dimensional (3D) spherical colormaps for tract tracing tasks (uniform gray, absolute, eigenmaps, and Boy's surface embedding). We found that extended-blackbody, coolwarm, and blackbody remain the best three approaches for identifying ensemble average in 3D. Isoluminant-rainbow colormap led to the same ensemble mean accuracy as other colormaps. However, more than 50 percent of the answers consistently had higher estimates of the ensemble average, independent of the mean values. The number of hues, not luminance, influences ensemble estimates of mean values. For ensemble orientation-tracing tasks, we found that both Boy's surface embedding (greatest spatial resolution and contrast) and absolute colormaps (lowest spatial resolution and contrast) led to more accurate answers than the eigenmaps scheme (medium resolution and contrast), acting as the uncanny-valley phenomenon of visualization design in terms of accuracy. Absolute colormap broadly used in brain science is a good default spherical colormap. We could conclude from our study that human visual processing of a chunk of colors differs from that of single colors.

[1]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[2]  B. E. Trumbo,et al.  A Theory for Coloring Bivariate Statistical Maps , 1981 .

[3]  Robert Sekuler,et al.  Coherent global motion percepts from stochastic local motions , 1984, Vision Research.

[4]  W. Cleveland,et al.  Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods , 1984 .

[5]  P. Hanrahan,et al.  Area and volume coherence for efficient visualization of 3D scalar functions , 1990, VVS.

[6]  Andrew P. Duchon,et al.  The human visual system averages speed information , 1992, Vision Research.

[7]  Gordon L. Kindlmann,et al.  Hue-balls and lit-tensors for direct volume rendering of diffusion tensor fields , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[8]  Sinisa Pajevic,et al.  Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: Application to white matter fiber tract mapping in the human brain , 1999, Magnetic resonance in medicine.

[9]  Graham D. Finlayson,et al.  Color by Correlation: A Simple, Unifying Framework for Color Constancy , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  D. Ariely Seeing Sets: Representation by Statistical Properties , 2001, Psychological science.

[11]  Bernice E. Rogowitz,et al.  The "Which Blair project": a quick visual method for evaluating perceptual color maps , 2001, Proceedings Visualization, 2001. VIS '01..

[12]  Erik Reinhard,et al.  Face-based luminance matching for perceptual colormap generation , 2002, IEEE Visualization, 2002. VIS 2002..

[13]  Carl-Fredrik Westin,et al.  Coloring of DT-MRI Fiber Traces Using Laplacian Eigenmaps , 2003, EUROCAST.

[14]  David H. Laidlaw,et al.  Visualizing Diffusion Tensor MR Images Using Streamtubes and Streamsurfaces , 2003, IEEE Trans. Vis. Comput. Graph..

[15]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[16]  Ron Kikinis,et al.  3D Slicer , 2012, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[17]  Robert S. Laramee,et al.  The State of the Art in Flow Visualization: Dense and Texture‐Based Techniques , 2004, Comput. Graph. Forum.

[18]  Wencheng Wu,et al.  The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations , 2005 .

[19]  S. Mori,et al.  Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research , 2006, Neuron.

[20]  Daniel Acevedo Feliz,et al.  Subjective Quantification of Perceptual Interactions among some 2D Scientific Visualization Methods , 2006, IEEE Transactions on Visualization and Computer Graphics.

[21]  Bernhard Preim,et al.  Real-Time Illustration of Vascular Structures , 2006, IEEE Transactions on Visualization and Computer Graphics.

[22]  Hangyi Jiang,et al.  DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking , 2006, Comput. Methods Programs Biomed..

[23]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[24]  Heidrun Schumann,et al.  Task-Driven Color Coding , 2008, 2008 12th International Conference Information Visualisation.

[25]  Erik-Jan van der Linden,et al.  Generating Color Palettes using Intuitive Parameters , 2008, Comput. Graph. Forum.

[26]  A. Oliva,et al.  The Representation of Simple Ensemble Visual Features Outside the Focus of Attention , 2008, Psychological science.

[27]  P. Sachdev,et al.  Diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease: a review , 2008, Current opinion in neurology.

[28]  D. Burr,et al.  A Visual Sense of Number , 2007, Current Biology.

[29]  Valerio Pascucci,et al.  Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data , 2009, 2009 IEEE International Conference on Data Mining Workshops.

[30]  B. Bauer Does Stevens’s Power Law for Brightness Extend to Perceptual Brightness Averaging? , 2009 .

[31]  David H. Laidlaw,et al.  Online Submission ID: vis-1157 Comparing 3D Vector Field Visualization Methods: A User Study , 2022 .

[32]  David H. Laidlaw,et al.  Coloring 3D Line Fields Using Boy’s Real Projective Plane Immersion , 2009, IEEE Transactions on Visualization and Computer Graphics.

[33]  J. Chen,et al.  Visual Analysis of Dimensionality Reduction in an Interactive Virtual Environment for Exploring Bat Flight Kinematics , 2009, EGVE/ICAT/EuroVR.

[34]  Tamara Munzner,et al.  A Nested Model for Visualization Design and Validation , 2009, IEEE Transactions on Visualization and Computer Graphics.

[35]  Min H. Kim,et al.  Modeling human color perception under extended luminance levels , 2009, ACM Trans. Graph..

[36]  Andrew Mercer,et al.  Noodles: A Tool for Visualization of Numerical Weather Model Ensemble Uncertainty , 2010, IEEE Transactions on Visualization and Computer Graphics.

[37]  Tobias Isenberg,et al.  DTI in Context: Illustrating Brain Fiber Tracts In Situ , 2010, Comput. Graph. Forum.

[38]  Krzysztof Z. Gajos,et al.  Evaluation of Artery Visualizations for Heart Disease Diagnosis , 2011, IEEE Transactions on Visualization and Computer Graphics.

[39]  Oliver Wright Effects of stimulus range on color categorization , 2011 .

[40]  Samuel S. Silva,et al.  Using color in visualization: A survey , 2011, Comput. Graph..

[41]  David H. Laidlaw,et al.  VisBubbles: a workflow-driven framework for scientific data analysis of time-varying biological datasets , 2011, SA '11.

[42]  Nicolas Robitaille,et al.  When more is less: extraction of summary statistics benefits from larger sets. , 2011, Journal of vision.

[43]  G. Alvarez Representing multiple objects as an ensemble enhances visual cognition , 2011, Trends in Cognitive Sciences.

[44]  Paul Rosen,et al.  From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches , 2011, WoCoUQ.

[45]  Christopher G. Healey,et al.  Exploring ensemble visualization , 2012, Visualization and Data Analysis.

[46]  David H. Laidlaw,et al.  Effects of illumination, texture, and motion on task performance in 3D tensor-field streamtube visualizations , 2012, 2012 IEEE Pacific Visualization Symposium.

[47]  David Whitney,et al.  Ensemble perception: Summarizing the scene and broadening the limits of visual processing. , 2012 .

[48]  Robert S. Laramee,et al.  Mesh-Driven Vector Field Clustering and Visualization: An Image-Based Approach , 2012, IEEE Transactions on Visualization and Computer Graphics.

[49]  David H. Laidlaw,et al.  Effects of Stereo and Screen Size on the Legibility of Three-Dimensional Streamtube Visualization , 2012, IEEE Transactions on Visualization and Computer Graphics.

[50]  T. J. Jankun-Kelly,et al.  A 2D Flow Visualization User Study Using Explicit Flow Synthesis and Implicit Task Design , 2012, IEEE Transactions on Visualization and Computer Graphics.

[51]  Steven Franconeri,et al.  Comparing averages in time series data , 2012, CHI.

[52]  Tobias Isenberg,et al.  A Systematic Review on the Practice of Evaluating Visualization , 2013, IEEE Transactions on Visualization and Computer Graphics.

[53]  Markus F. Neumann,et al.  Viewers extract mean and individual identity from sets of famous faces , 2013, Cognition.

[54]  Donald H. House,et al.  Visualizing Uncertainty in Predicted Hurricane Tracks , 2013 .

[55]  Markus Christen,et al.  Colorful brains: 14years of display practice in functional neuroimaging , 2013, NeuroImage.

[56]  Jian Chen,et al.  An Interactive Method for Generating Harmonious Color Schemes , 2014 .

[57]  M. Webster,et al.  Perceiving the average hue of color arrays. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[58]  A. Franklin,et al.  Getting the gist of multiple hues: metric and categorical effects on ensemble perception of hue. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[59]  Rita Borgo,et al.  Order of Magnitude Markers: An Empirical Study on Large Magnitude Number Detection , 2014, IEEE Transactions on Visualization and Computer Graphics.

[60]  Timothy F. Brady,et al.  Individual differences in ensemble perception reveal multiple, independent levels of ensemble representation. , 2015, Journal of experimental psychology. General.

[61]  A. Franklin,et al.  Effects of ensemble complexity and perceptual similarity on rapid averaging of hue. , 2015, Journal of vision.

[62]  Kenneth Moreland,et al.  Why We Use Bad Color Maps and What You Can Do About It , 2016, HVEI.

[63]  Timo Ropinski,et al.  A Survey of Perceptually Motivated 3D Visualization of Medical Image Data , 2016, Comput. Graph. Forum.

[64]  Steven Franconeri,et al.  Four types of ensemble coding in data visualizations. , 2016, Journal of vision.

[65]  Gennady L. Andrienko,et al.  Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics , 2016, Inf. Syst..

[66]  Allison Yamanashi Leib,et al.  Fast ensemble representations for abstract visual impressions , 2016, Nature Communications.

[67]  Alex Endert,et al.  Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes , 2016, IEEE Transactions on Visualization and Computer Graphics.

[68]  Charles D. Hansen,et al.  A Survey of Colormaps in Visualization , 2016, IEEE Transactions on Visualization and Computer Graphics.

[69]  Thomas E. Nichols,et al.  Heterochronicity of white matter development and aging explains regional patient control differences in schizophrenia , 2016, Human brain mapping.

[70]  A. Chetverikov,et al.  Representing Color Ensembles , 2017, Psychological science.

[71]  Henan Zhao,et al.  Bivariate Separable-Dimension Glyphs can Improve Visual Analysis of Holistic Features , 2017, ArXiv.

[72]  Elmar Eisemann,et al.  Overview + Detail Visualization for Ensembles of Diffusion Tensors , 2017, Comput. Graph. Forum.

[73]  Jian Chen,et al.  Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields , 2017, IEEE Transactions on Visualization and Computer Graphics.

[74]  Roxana Bujack,et al.  Evaluating the Perceptual Uniformity of Color Sequences for Feature Discrimination , 2017, EuroRV³@EuroVis.

[75]  David H. Laidlaw,et al.  Colorgorical: Creating discriminable and preferable color palettes for information visualization , 2017, IEEE Transactions on Visualization and Computer Graphics.

[76]  James P. Ahrens,et al.  The Good, the Bad, and the Ugly: A Theoretical Framework for the Assessment of Continuous Colormaps , 2018, IEEE Transactions on Visualization and Computer Graphics.

[77]  Danielle Albers Szafir,et al.  Modeling Color Difference for Visualization Design , 2018, IEEE Transactions on Visualization and Computer Graphics.

[78]  D. Whitney,et al.  Ensemble Perception , 2018, Annual review of psychology.

[79]  Junpeng Wang,et al.  Visualization and Visual Analysis of Ensemble Data: A Survey , 2019, IEEE Transactions on Visualization and Computer Graphics.