Visual analytics for the interpretation of fluency tests during Alzheimer evaluation

International audienceA possible way to evaluate the progress of Alzheimer disease is to conduct the Isaac set test [13, 14]. In this activity, patients are asked to cite the largest possible number of city names within a minute. Since the city names are handwritten very quickly by a medical practitioner some cities are abbreviated or poorly written. In order to analyze such data, medical practitioners need to digitize the notes first and clean the dataset. Because these tasks are intricate and error prone we propose a novel set of tools, involving interactive visualization techniques, to help medical practitioners in the digitization and data-cleaning process. This system will be tested as part of an ongoing longitudinal study involving 9500 patients

Visual Systems for Interactive Exploration and Mining of Large-Scale Neuroimaging Data Archives

While technological advancements in neuroimaging scanner engineering have improved the efficiency of data acquisition, electronic data capture methods will likewise significantly expedite the populating of large-scale neuroimaging databases. As they do and these archives grow in size, a particular challenge lies in examining and interacting with the information that these resources contain through the development of compelling, user-driven approaches for data exploration and mining. In this article, we introduce the informatics visualization for neuroimaging (INVIZIAN) framework for the graphical rendering of, and dynamic interaction with the contents of large-scale neuroimaging data sets. We describe the rationale behind INVIZIAN, detail its development, and demonstrate its usage in examining a collection of over 900 T1-anatomical magnetic resonance imaging (MRI) image volumes from across a diverse set of clinical neuroimaging studies drawn from a leading neuroimaging database. Using a collection of cortical surface metrics and means for examining brain similarity, INVIZIAN graphically displays brain surfaces as points in a coordinate space and enables classification of clusters of neuroanatomically similar MRI images and data mining. As an initial step toward addressing the need for such user-friendly tools, INVIZIAN provides a highly unique means to interact with large quantities of electronic brain imaging archives in ways suitable for hypothesis generation and data mining

Explorative coastal oceanographic visual analytics : oceans of data

The widely acknowledged challenge to data analysis and understanding, resulting from the exponential increase in volumes of data generated by increasingly complex modelling and sampling systems, is a problem experienced by many researchers, including ocean scientists. The thesis explores a visualization and visual analytics solution for predictive studies of coastal shelf and estuarine modelled, hydrodynamics undertaken to understand sea level rise, as a contribution to wider climate change studies, and to underpin coastal zone planning, flood prevention and extreme event management. But these studies are complex and require numerous simulations of estuarine hydrodynamics, generating extremely large datasets of multi-field data. This type\ud of data is acknowledged as difficult to visualize and analyse, as its numerous attributes present significant computational challenges, and ideally require a wide range of approaches to provide the necessary insight. These challenges are not easily overcome with the current visualization and analysis methodologies employed by coastal shelf hydrodynamic researchers, who use several software systems to generate graphs, each taking considerable time to operate, thus it is difficult to explore different scenarios and explore the data interactively and visually. The thesis, therefore, develops novel visualization and visual analytics techniques to help researchers overcome the limitations of existing methods (for example in understanding key tidal components); analyse data in a timely manner and explore different scenarios. There were a number of challenges to this: the size of the data, resulting in lengthy computing time, also many data values becoming plotted on one pixel (overplotting). The thesis presents: (1) a new visualization framework (VINCA) using caching and hierarchical aggregation techniques to make the data more interactive, plus explorative, coordinated multiple views, to enable the scientists to explore the data. (2) A novel estuarine transect profiler and flux tool, which provides instantaneous flux calculations across an estuary. Measures of flux are of great significance in oceanographic studies, yet are notoriously difficult and time consuming to calculate with the commonly used tools. This derived data is added back into the database for further investigation and analysis. (3) New views, including a novel, dynamic, spatially aggregated Parallel Coordinate Plots (Sa-PCP), are developed to provide different perspectives of the spatial, time dependent data, also methodologies for developing high-quality (journal ready) output from the visualization tool. Finally, (4) the dissertation explored the use of hierarchical data-structures and caching techniques to enable fast analysis on a desktop computer and to overcome the overplotting challenge for this data

An algorithmic framework for visualising and exploring multidimensional data

To help understand multidimensional data, information visualisation techniques are often applied to take advantage of human visual perception in exposing latent structure. A popular means of presenting such data is via two-dimensional scatterplots where the inter-point proximities reflect some notion of similarity between the entities represented. This can result in potentially interesting structure becoming almost immediately apparent. Traditional algorithms for carrying out this dimension reduction tend to have different strengths and weaknesses in terms of run times and layout quality. However, it has been found that the combination of algorithms can produce hybrid variants that exhibit significantly lower run times while maintaining accurate depictions of high-dimensional structure. The author's initial contribution in the creation of such algorithms led to the design and implementation of a software system (HIVE) for the development and investigation of new hybrid variants and the subsequent analysis of the data they transform. This development was motivated by the fact that there are potentially many hybrid algorithmic combinations to explore and therefore an environment that is conductive to their development, analysis and use is beneficial not only in exploring the data they transform but also in exploring the growing number of visualisation tools that these algorithms beget. This thesis descries three areas of the author's contribution to the field of information visualisation. Firstly, work on hybrid algorithms for dimension reduction is presented and their analysis shows their effectiveness. Secondly, the development of a framework for the creation of tailored hybrid algorithms is illustrated. Thirdly, a system embodying the framework, providing an environment conductive to the development, evaluation and use of the algorithms is described. Case studies are provided to demonstrate how the author and others have used and found value in the system across areas as diverse as environmental science, social science and investigative psychology, where multidimensional data are in abundance