Visual Analysis of Multi-Field Data

Abstract

This thesis investigates methods for the visualization of multi-field medical data. In the medical field, data complexity has been growing consistently over the past years. Not only the size of the data grows, but also the need to visualize beyond traditional boundaries. We present a number of novel facets that encompass a general approach to the exploration of multi-field data. We strongly believe that human-in-the-loop visual data analysis on large and complex datasets is best aided by multiple linked different representations. The presented techniques demonstrate how complex data from multiple modalities can be visualized and interactively explored. We explore the use of linked selections to aid in reducing the complexity of the visualizations. Using multiple-linked views, we can integrate multiple orthogonal representations of the data simultaneously. We have applied aforementioned techniques in the design and implementation of a number of prototype frameworks, with applications ranging from brain imaging for neurosurgical planning to the study of the behavior of marine animals through the use of sensor data. We also present a conceptual framework for studying complex longitudinal data, by means of aggregation and multi-level visualization. We successfully adapted techniques from information visualization in order to use them on datasets that are orders of magnitude larger than they are originally used for.