Measuring and Modeling the Feature Detection Threshold Functions of Colormaps

Color Maps: Facilitating the Clinical Impact of Quantitative MRI

Presenting quantitative data using non-standardized color maps potentially results in unrecognized misinterpretation of data. Clinically meaningful color maps should intuitively and inclusively represent data without misleading interpretation. Uniformity of the color gradient for color maps is critically important. Maximal color and lightness contrast, readability for color vision-impaired individuals, and recognizability of the color scheme are highly desirable features. This article describes the use of color maps in five key quantitative MRI techniques: relaxometry, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI, MR elastography (MRE), and water-fat MRI. Current display practice of color maps is reviewed and shortcomings against desirable features are highlighted. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Toward the validation of crowdsourced experiments for lightness perception

Crowdsource platforms have been used to study a range of perceptual stimuli such as the graphical perception of scatterplots and various aspects of human color perception. Given the lack of control over a crowdsourced participant's experimental setup, there are valid concerns on the use of crowdsourcing for color studies as the perception of the stimuli is highly dependent on the stimulus presentation. Here, we propose that the error due to a crowdsourced experimental design can be effectively averaged out because the crowdsourced experiment can be accommodated by the Thurstonian model as the convolution of two normal distributions, one that is perceptual in nature and one that captures the error due to variability in stimulus presentation. Based on this, we provide a mathematical estimate for the sample size needed to produce a crowdsourced experiment with the same power as the corresponding in-person study. We tested this claim by replicating a large-scale, crowdsourced study of human lightness perception with a diverse sample with a highly controlled, in-person study with a sample taken from psychology undergraduates. Our claim was supported by the replication of the results from the latter. These findings suggest that, with sufficient sample size, color vision studies may be completed online, giving access to a larger and more representative sample. With this framework at hand, experimentalists have the validation that choosing either many online participants or few in person participants will not sacrifice the impact of their results.

Rainbow Colormaps Are Not All Bad

Some 15 years ago, Visualization Viewpoints published an influential article titled Rainbow Color Map (Still) Considered Harmful (Borland and Taylor, 2007). The paper argued that the "rainbow colormap's characteristics of confusing the viewer, obscuring the data and actively misleading interpretation make it a poor choice for visualization." Subsequent articles often repeat and extend these arguments, so much so that avoiding rainbow colormaps, along with their derivatives, has become dogma in the visualization community. Despite this loud and persistent recommendation, scientists continue to use rainbow colormaps. Have we failed to communicate our message, or do rainbow colormaps offer advantages that have not been fully appreciated? We argue that rainbow colormaps have properties that are underappreciated by existing design conventions. We explore key critiques of the rainbow in the context of recent research to understand where and how rainbows might be misunderstood. Choosing a colormap is a complex task, and rainbow colormaps can be useful for selected applications.

Data-Driven Colormap Adjustment for Exploring Spatial Variations in Scalar Fields

Colormapping is an effective and popular visualization technique for analyzing patterns in scalar fields. Scientists usually adjust a default colormap to show hidden patterns by shifting the colors in a trial-and-error process. To improve efficiency, efforts have been made to automate the colormap adjustment process based on data properties (e.g., statistical data value or histogram distribution). However, as the data properties have no direct correlation to the spatial variations, previous methods may be insufficient to reveal the dynamic range of spatial variations hidden in the data. To address the above issues, we conduct a pilot analysis with domain experts and summarize three requirements for the colormap adjustment process. Based on the requirements, we formulate colormap adjustment as an objective function, composed of a boundary term and a fidelity term, which is flexible enough to support interactive functionalities. We compare our approach with alternative methods under a quantitative measure and a qualitative user study (25 participants), based on a set of data with broad distribution diversity. We further evaluate our approach via three case studies with six domain experts. Our method is not necessarily more optimal than alternative methods of revealing patterns, but rather is an additional color adjustment option for exploring data with a dynamic range of spatial variations.

Rainbow Colormaps: What are they good and bad for?

Guidelines for color use in quantitative visualizations have strongly discouraged the use of rainbow colormaps, arguing instead for smooth designs that do not induce visual discontinuities or implicit color categories. However, the empirical evidence behind this argument has been mixed and, at times, even contradictory. In practice, rainbow colormaps are widely used, raising questions about the true utility or dangers of such designs. We study how color categorization impacts the interpretation of scalar fields. We first introduce an approach to detect latent categories in colormaps. We hypothesize that the appearance of color categories in scalar visualizations can be beneficial in that they enhance the perception of certain features, although at the cost of rendering other features less noticeable. In three crowdsourced experiments, we show that observers are more likely to discriminate global, distributional features when viewing colorful scales that induce categorization (e.g., rainbow or diverging schemes). Conversely, when seeing the same data through a less colorful representation, observers are more likely to report localized features defined by small variations in the data. Participants showed awareness of these different affordances, and exhibited bias for exploiting the more discriminating colormap, given a particular feature type. Our results demonstrate costs and benefits for rainbows (and similarly colorful schemes), suggesting that their complementary utility for analyzing scalar data should not be dismissed. In addition to explaining potentially valid uses of rainbow, our study provides actionable guidelines, including on when such designs can be more harmful than useful. Data and materials are available at https://osf.io/xjhtf.

Rainbow Dash: Intuitiveness, Interpretability and Memorability of the Rainbow Color Scheme in Visualization

After demonstrating that rainbow colors are still commonly used in scientific publications, we comparatively evaluate the rainbow and sequential color schemes on choropleth and isarithmic maps in an empirical user study with 544 participants to examine if a) people intuitively associate order for the colors in these schemes, b) they can successfully conduct perceptual and semantic map reading and recall tasks with quantitative data where order may have implicit or explicit importance. We find that there is little to no agreement in ordering of rainbow colors while sequential colors are indeed intuitively ordered by the participants with a strong dark is more bias. Sequential colors facilitate most quantitative map reading tasks better than the rainbow colors, whereas rainbow colors competitively facilitate extracting specific values from a map, and may support hue recall better than sequential. We thus contribute to dark- versus light is more bias debate, demonstrate why and when rainbow colors may impair performance, and add further nuance to our understanding of this highly popular, yet highly criticized color scheme.

Development of Bigels Based on Date Palm-Derived Cellulose Nanocrystal-Reinforced Guar Gum Hydrogel and Sesame Oil/Candelilla Wax Oleogel as Delivery Vehicles for Moxifloxacin

Bigels are biphasic semisolid systems that have been explored as delivery vehicles in the food and pharmaceutical industries. These formulations are highly stable and have a longer shelf-life than emulsions. Similarly, cellulose-based hydrogels are considered to be ideal for these formulations due to their biocompatibility and flexibility to mold into various shapes. Accordingly, in the present study, the properties of an optimized guar gum hydrogel and sesame oil/candelilla wax oleogel-based bigel were tailored using date palm-derived cellulose nanocrystals (dp-CNC). These bigels were then explored as carriers for the bioactive molecule moxifloxacin hydrochloride (MH). The preparation of the bigels was achieved by mixing guar gum hydrogel and sesame oil/candelilla wax oleogel. Polarizing microscopy suggested the formation of the hydrogel-in-oleogel type of bigels. An alteration in the dp-CNC content affected the size distribution of the hydrogel phase within the oleogel phase. The colorimetry studies revealed the yellowish-white color of the samples. There were no significant changes in the FTIR functional group positions even after the addition of dp-CNC. In general, the incorporation of dp-CNC resulted in a decrease in the impedance values, except BG3 that had 15 mg dp-CNC in 20 g bigel. The BG3 formulation showed the highest firmness and fluidity. The release of MH from the bigels was quasi-Fickian diffusion mediated. BG3 showed the highest release of the drug. In summary, dp-CNC can be used as a novel reinforcing agent for bigels.

The non-Riemannian nature of perceptual color space

For over 100 y, the scientific community has adhered to a paradigm, introduced by Riemann and furthered by Helmholtz and Schrodinger, where perceptual color space is a three-dimensional Riemannian space. This implies that the distance between two colors is the length of the shortest path that connects them. We show that a Riemannian metric overestimates the perception of large color differences because large color differences are perceived as less than the sum of small differences. This effect, called diminishing returns, cannot exist in a Riemannian geometry. Consequently, we need to adapt how we model color differences, as the current standard, ΔE, recognized by the International Commission for Weights and Measures, does not account for diminishing returns in color difference perception.

The scientific community generally agrees on the theory, introduced by Riemann and furthered by Helmholtz and Schrödinger, that perceived color space is not Euclidean but rather, a three-dimensional Riemannian space. We show that the principle of diminishing returns applies to human color perception. This means that large color differences cannot be derived by adding a series of small steps, and therefore, perceptual color space cannot be described by a Riemannian geometry. This finding is inconsistent with the current approaches to modeling perceptual color space. Therefore, the assumed shape of color space requires a paradigm shift. Consequences of this apply to color metrics that are currently used in image and video processing, color mapping, and the paint and textile industries. These metrics are valid only for small differences. Rethinking them outside of a Riemannian setting could provide a path to extending them to large differences. This finding further hints at the existence of a second-order Weber–Fechner law describing perceived differences.

Manifold Approximating Graph Interpolation of Cardiac Local Activation Time

Objective:

Local activation time (LAT) mapping of cardiac chambers is vital for targeted treatment of cardiac arrhythmias in catheter ablation procedures. Current methods require too many LAT observations for an accurate interpolation of the necessarily sparse LAT signal extracted from intracardiac electrograms (EGMs). Additionally, conventional performance metrics for LAT interpolation algorithms do not accurately measure the quality of interpolated maps. We propose, first, a novel method for spatial interpolation of the LAT signal which requires relatively few observations; second, a realistic sub-sampling protocol for LAT interpolation testing; and third, a new color-based metric for evaluation of interpolation quality that quantifies perceived differences in LAT maps.

Methods:

We utilize a graph signal processing framework to reformulate the irregular spatial interpolation problem into a semi-supervised learning problem on the manifold with a closed-form solution. The metric proposed uses a color difference equation and color theory to quantify visual differences in generated LAT maps.

Results:

We evaluate our approach on a dataset consisting of seven LAT maps from four patients obtained by the CARTO electroanatomic mapping system during premature ventricular complex (PVC) ablation procedures. Random sub-sampling and re-interpolation of the LAT observations show excellent accuracy for relatively few observations, achieving on average 6% lower error than state-of-the-art techniques for only 100 observations.

Conclusion:

Our study suggests that graph signal processing methods can improve LAT mapping for cardiac ablation procedures.

Significance:

The proposed method can reduce patient time in surgery by decreasing the number of LAT observations needed for an accurate LAT map.

Visualization of Human Spine Biomechanics for Spinal Surgery

We propose a visualization application, designed for the exploration of human spine simulation data. Our goal is to support research in biomechanical spine simulation and advance efforts to implement simulation-backed analysis in surgical applications. Biomechanical simulation is a state-of-the-art technique for analyzing load distributions of spinal structures. Through the inclusion of patient-specific data, such simulations may facilitate personalized treatment and customized surgical interventions. Difficulties in spine modelling and simulation can be partly attributed to poor result representation, which may also be a hindrance when introducing such techniques into a clinical environment. Comparisons of measurements across multiple similar anatomical structures and the integration of temporal data make commonly available diagrams and charts insufficient for an intuitive and systematic display of results. Therefore, we facilitate methods such as multiple coordinated views, abstraction and focus and context to display simulation outcomes in a dedicated tool. By linking the result data with patient-specific anatomy, we make relevant parameters tangible for clinicians. Furthermore, we introduce new concepts to show the directions of impact force vectors, which were not accessible before. We integrated our toolset into a spine segmentation and simulation pipeline and evaluated our methods with both surgeons and biomechanical researchers. When comparing our methods against standard representations that are currently in use, we found increases in accuracy and speed in data exploration tasks. in a qualitative review, domain experts deemed the tool highly useful when dealing with simulation result data, which typically combines time-dependent patient movement and the resulting force distributions on spinal structures.

The misuse of colour in science communication

The accurate representation of data is essential in science communication. However, colour maps that visually distort data through uneven colour gradients or are unreadable to those with colour-vision deficiency remain prevalent in science. These include, but are not limited to, rainbow-like and red-green colour maps. Here, we present a simple guide for the scientific use of colour. We show how scientifically derived colour maps report true data variations, reduce complexity, and are accessible for people with colour-vision deficiencies. We highlight ways for the scientific community to identify and prevent the misuse of colour in science, and call for a proactive step away from colour misuse among the community, publishers, and the press.

A neutrosophic-entropy based clustering algorithm (NEBCA) with HSV color system: A special application in segmentation of Parkinson's disease (PD) MR images

BACKGROUND AND OBJECTIVES

Brain MR images consist of three major regions: gray matter, white matter and cerebrospinal fluid. Medical experts make decisions on different serious diseases by evaluating the developments in these areas. One of the significant approaches used in analyzing the MR images were segmenting the regions. However, their segmentation suffers from two major problems as: (a) the boundaries of their gray matter and white matter regions are ambiguous in nature, and (b) their regions are formed with unclear inhomogeneous gray structures. For these reasons, diagnosis of critical diseases is often very difficult.

METHODS

This study presented a new method for MR image segmentation, which consisted of two main parts as: (a) neutrosophic-entropy based clustering algorithm (NEBCA), and (b) HSV color system. The NEBCA's role in this study was to perform segmentation of MR regions, while HSV color system was used to provide better visual representation of features in segmented regions.

RESULTS

Application of the proposed method was demonstrated in 30 different MR images of Parkinson's disease (PD). Experimental results were presented individually for the NEBCA and HSV color system. The performance of the proposed method was evaluated in terms of statistical metrics used in an image segmentation domain. Experimental results, including statistical analysis reflected the efficiency of the proposed method over the existing well-known image segmentation methods available in literature. For the proposed method and existing methods, the average CPU time (in nanosecond) was computed and it was found that the proposed method consumed less time to segment MR images.

CONCLUSION

The proposed method can effectively segment different regions of MR images and can very clearly represent those segmented regions.

Crowdsourcing visual perception experiments: a case of contrast threshold

Crowdsourcing has commonly been used for psychological research but not for studies on sensory perception. A reason is that in online experiments, one cannot ensure that the rigorous settings required for the experimental environment are replicated. The present study examined the suitability of online experiments on basic visual perception, particularly the contrast threshold. We conducted similar visual experiments in the laboratory and online, employing three experimental conditions. The first was a laboratory experiment, where a small sample of participants (n = 24; laboratory condition) completed a task with 10 iterations. The other two conditions were online experiments: participants were either presented with a task without repetition of trials (n = 285; online non-repetition condition) or one with 10 iterations (n = 166; online repetition condition). The results showed significant equivalence in the contrast thresholds between the laboratory and online repetition conditions, although a substantial amount of data needed to be excluded from the analyses in the latter condition. The contrast threshold was significantly higher in the online non-repetition condition compared with the laboratory and online repetition conditions. To make crowdsourcing more suitable for investigating the contrast threshold, ways to reduce data wastage need to be formulated.