Translating Cancer Research into Clinical Practice: A Framework for Analyzing and Modeling Cancer from Imaging Data

Design and development of data visualization systems in a workstation environment

In recent years, the combination of databases, data analysis, and Internet technologies has greatly enriched the functionalities of GIS and facilitated the dissemination of geospatial information, hi this paper, we propose a web-based data visualization and analysis system to manage the watershed of the Occoquan Basin in Northern Virginia. This system distinguishes itself from others in the following respects. First, it enables near real-time data collection and dissemination. Second, data-query tools are embedded in the system to support effective data analysis and visualization functionalities. In addition to regular queries, aggregation queries have been developed to provide summary views of historical data with various granularities. Third, the visualization and analysis tools can be accessed via the Internet. WWW technologies, such as active web pages and Java programming, provide user-friendly interfaces for browsing, plotting, comparing, and downloading information of interest, without the need of dedicated GIS software. This system can render information to support many other applications, including pollution control and biochemical detection.

The behavioral patterns of human in buildings influence the rational setting of space and function dramatically. However, due to the lack of data acquisition methods and data accuracy, big data analysis and visualization research in the microscopic aspects of indoor space is hampered. With the maturity of indoor positioning technology, UWB (Ultra Wideband) positioning technology based on narrow pulse has the characteristics of high transmission rate, low transmit power and strong penetrating ability, which provides more accurate results for the behavior data acquisition in indoor space. In this research, the big data thinking has been introduced into the behavioral performance analysis process. Therefore, data acquisition, data storage and management, behavioral data visualization and machine learning algorithms are integrated into a set of behavioral data analysis and visualization system, to quantitative research the behavioral characteristics of visitors in the exhibition hall by the on-site experiment .

We have developed efficient methods related to the generation of geometrical models from 3D image data. This includes visualization of the primary data and the resulting models as well as image segmentation, extraction of line structures and triangulation of labelings. Our primary motivation has been to create a practically usable working environment for researchers in neurobiology, but we have verified the generality of the methods. We have worked in the following fields: Visualization: We have implemented visualization techniques for the primary data types occurring in our application: three dimensional image data, polygonal surfaces, and polyline trees, as well as arbitrary combinations of these. Specifically, we have implemented different slicing techniques, color mapping techniques, isosurfacing and volume rendering algorithms. We have shown how maximum intensity projections can be applied to efficiently visualize sparse data sets like neuronal dendritic trees and we have presented a new technique for effective shading of line-structures. Furthermore we derive and propose a transparency model which significantly enhances the shape perception. Image Segmentation: We have designed and implemented a complete interactive image segmentation tool. We have discussed why efficient interactive and semi- automatic segmentation tools are essential for practical applications and discussed how they can be complemented with interpolation and extrapolation methods. We have proposed new distance map based interpolation schemes and shown results. Line Extraction: We have developed an interactive method for the extraction of line-like structures based on MIP. Geometry Reconstruction: We have proposed a new method for the generation of consistent, intersection free, and closed surfaces from non-binary labelings. This is complemented with our new algorithm to compute sub-voxel positions for the intersection points, which lead to smooth surfaces, which are still consistent with the given labeling. Furthermore we have proposed a flexible and easy-to-implement method to create polygonal models from thickness- annotated graph structures for high quality rendering. Registration and Averaging: We have discussed, implemented, and used existing methods for correlation based and landmark based registration methods, and explained how they can be used in the context of our application for integration, comparison, and averaging. We have proposed a non-affine registration procedure and we have shown average intensity maps generated with this method. Applications: We have presented biological applications based on our results. The described methods have been implemented within the Amira visualization system..

Under the background of big data era, ports have realized the informatization of transport data, and has accumulated a large amount of transport data. The potential value contained in the port transportation data needs to be excavated, and a large number of external data also needs to be obtained. However, the traditional statistical analysis system can no longer meet the demand. In order to fully tap the potential value of port data, this paper designs and implements a port data analysis and visualization system to help enterprises master the trend of material flow. The system development is based on Python language. Pandas is used for data analysis, Flask is used to implement the MVC framework, ECharts is used for chart display, and data is imported from the business database to the MySQL database.

A web based MDSplus data analysis and visualization system for EAST

In order to meet the needs of data analysis and visualization to assist mobile network operation decision-making, telecom operators have established several mobile network index analysis tools or platforms. However, the network data analysis efficiency of planning, construction, maintenance and optimization is still low, and demand-oriented visualization means are still insufficient. This paper designs a mobile network data analysis and visualization system. The designed system aims at addressing the problems that mobile network has various types of data. The designed system can make data easy to manage, improve the data analysis efficiency and the flexibility of data visualization for telecom operators.

Objectives: To evaluate white matter hyperintensities (WMH) quantification reproducibility from multiple aspects of view and examine the effects of scan–rescan procedure, types of scanner, imaging protocols, scanner software upgrade, and automatic segmentation tools on WMH quantification results using magnetic resonance imaging (MRI).Methods: Six post-stroke subjects (4 males; mean age = 62.8, range = 58–72 years) were scanned and rescanned with both 3D T1-weighted, 2D and 3D T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) MRI across four different MRI scanners within 12 h. Two automated WMH segmentation and quantification tools were used to measure WMH volume based on each MR scan. Robustness was assessed using the coefficient of variation (CV), Dice similarity coefficient (DSC), and intra-class correlation (ICC).Results: Experimental results show that the best reproducibility was achieved by using 3D T2-FLAIR MRI under intra-scanner setting with CV ranging from 2.69 to 2.97%, while the largest variability resulted from comparing WMH volumes measured based on 2D T2-FLAIR MRI with those of 3D T2-FLAIR MRI, with CV values in the range of 15.62%–29.33%. The WMH quantification variability based on 2D MRIs is larger than 3D MRIs due to their large slice thickness. The DSC of WMH segmentation labels between intra-scanner MRIs ranges from 0.63 to 0.77, while that for inter-scanner MRIs is in the range of 0.63–0.65. In addition to image acquisition, the choice of automatic WMH segmentation tool also has a large impact on WMH quantification.Conclusion: WMH reproducibility is one of the primary issues to be considered in multicenter and longitudinal studies. The study provides solid guidance in assisting multicenter and longitudinal study design to achieve meaningful results with enough power.

Music streaming platforms dominate digital consumption but offer users limited and superficial insight into their own listening behaviors. This data opacity restricts user autonomy and reinforces a dependency on opaque algorithmic recommendations, creating a distinct need for transparent, user-centric analytical tools. This paper presents Listen, a web-based system developed through a Design Science Research methodology to address this gap. The system securely integrates with the Spotify API to retrieve granular, longitudinal user data and employs advanced data visualization techniques within a fully responsive, intuitive interface. The resulting artifact provides interactive dashboards that empower users to conduct in-depth analysis of their most-listened artists and tracks across customizable timeframes and to explore complex audio features through accessible graphical representations. Our findings demonstrate that by prioritizing usability and analytical depth, Listen successfully provides users with greater transparency and control over their personal data, serving as a robust model for bridging the critical gap between data availability and user insight in digital media platforms.

The combination of 3D acquisition (terrestrial and airborne LiDAR, structured light, structure-from-motion) and 2D imaging (photographic, multispectral, panoramic, orthorectified, reflectance transformation) techniques allows the geometry, appearance and other aspects of culturally significant sites to be objectively documented. Traditionally, these data are usually transformed into models such as 3D textured meshes before they are visualized or analyzed—an often time- and effort-intensive process. We propose a system for the direct visualization and analysis of such data, allowing the different aspects recorded to be layered together, and co-visualized with annotations and other relevant information. We describe the required technical foundations, including gigapoint and gigapixel visualization pipelines that enable the dynamic layering of high-resolution imagery over massive minimally-processed LiDAR point clouds that serve as the base spatial layer. In particular, we introduce the pointbuffer—a GPU-resident viewdependent point cache—as the foundation of our gigapoint pipeline, and outline the use of virtual texturing for draping of gigapixel imagery onto point clouds. Finally, we present case studies from sites in Jordan and Italy.

The authors present in this work a large-scale system for space-time visualization, monitoring, modeling and analysis of epidemic data using a Grid platform. This system, dubbed IntegraEPI, is capable to integrate data from heterogeneous databases related to epidemic analysis and to make available analytical and computational methods to increase the predicting capability of the public heath system, in order to optimize its activities and resources when dealing with epidemic outbreak and prevention. This system, differently of what has been proposed before, is integrated and consequently it enables the construction of detailed predictive models of the dynamics of disease spreading. With the help of IntegraEPI, Health authorities will be able to decide about a set of possible actions that will be previously tested in a virtual population interacting in an urban infrastructure, considering its environmental factors, and finally compare the simulated data to consolidated data of real epidemic dynamics.

BackgroundRecent advances with high-throughput methods in life-science research have increased the need for automatized data analysis and visual exploration techniques. Sophisticated bioinformatics tools are essential to deduct biologically meaningful interpretations from the large amount of experimental data, and help to understand biological processes.ResultsWe present VANTED, a tool for the visualization and analysis of networks with related experimental data. Data from large-scale biochemical experiments is uploaded into the software via a Microsoft Excel-based form. Then it can be mapped on a network that is either drawn with the tool itself, downloaded from the KEGG Pathway database, or imported using standard network exchange formats. Transcript, enzyme, and metabolite data can be presented in the context of their underlying networks, e. g. metabolic pathways or classification hierarchies. Visualization and navigation methods support the visual exploration of the data-enriched networks. Statistical methods allow analysis and comparison of multiple data sets such as different developmental stages or genetically different lines. Correlation networks can be automatically generated from the data and substances can be clustered according to similar behavior over time. As examples, metabolite profiling and enzyme activity data sets have been visualized in different metabolic maps, correlation networks have been generated and similar time patterns detected. Some relationships between different metabolites were discovered which are in close accordance with the literature.ConclusionVANTED greatly helps researchers in the analysis and interpretation of biochemical data, and thus is a useful tool for modern biological research. VANTED as a Java Web Start Application including a user guide and example data sets is available free of charge at .

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.

Although there are a number of visualization systems to choose from when analyzing data, only a few of these allow for the integration of other visualization and analysis techniques. There are even fewer visualization toolkits, frameworks and developmental systems from which one can develop custom visualization applications. Even within the research community, scientists either use what they can from the available tools or start from scratch to define a program in which they are able to develop new or modified visualization techniques. This research presents a new general-purpose platform in which the design and experimentation of new visualization and analysis techniques is the focus, and where the sharing of and integration with other techniques becomes second nature. Here we introduce the Universal Visualization Platform (UVP) as a data visualization and analysis system supporting future research and modern discovery environments. Furthermore, we present a novel perspective on the Model-View-Controller programming paradigm that defines visualizations as views of models. Finally, we present a new layer model specifying the construction and manipulation of visualizations. Here, layers containing graphic elements that encode information are rendered together using composition operators to generate visual displays.

To address the increasing complexity in parallel and distributed systems and software, advances in performance technology towards more robust tools and broader, more portable implementations are needed. In doing so, new challenges for performance instrumentation, measurement, analysis, and visualization arise to address evolving requirements for how performance phenomena is observed and how performance data is used. This paper presents recent advances in the TAU performance system in four areas where improvements in performance technology are important: instrumentation control, performance mapping, performance interaction and steering, and performance databases. In the area of instrumentation control, we are concerned with the removal of instrumentation in cases of high measurement overhead. Our approach applies rule-based analysis of performance data in an iterative instrumentation process. Work on performance mapping focuses on measuring performance with respect to dynamic calling paths when the static callgraph cannot be determined prior to execution. We describe an online performance data access, analysis, and visualization system that will form the basis of a large-scale performance interaction and steering system. Finally, we describe our approach to the management of performance data in a database framework that supports multi-experiment analysis.