We study the effectiveness of stereoscopy and smooth motion as 3D cues for medical interpretation of vascular structures as obtained by 3D medical imaging techniques. We designed a user study where the user has to follow a path in a mazelike solid shaded 3D structure. The user controls rotation of the model. We measure user performance in terms of time taken and error rate. The experiment was executed with 32 (medical and non-medical) users. The results show that motion cue is more important than stereoscopy, and that stereoscopy has no added value when motion is already present, which is not consistent with previous experiments.

People have always relied on visual tools such as maps, charts and diagrams to better understand problems and solve them in less time. Continuous improvements in computer processing power and graphics capabilities have made it possible to incorporate a wide range of advanced visualization techniques in most computing application domains, including business, medicine, engineering and science.

With the era of Grid computing, data driven experiments and simulations have become very advanced and complicated. To allow specialists from various domains to deal with large datasets, aside from developing efficient extraction techniques, it is necessary to have available computational facilities to visualize and interact with the results of an extraction process. Having this in mind, we developed an Interactive Visualization Framework, which supports a service-oriented architecture. This framework allows, on one hand visualization experts to construct visualizations to view and interact with large datasets, and on the other hand end-users (e.g., medical specialists) to explore these visualizations irrespective of their geographical location and available computing resources. The image-based analysis of vascular disorders served as a case study for this project. The paper presents main research findings and reports on the current implementation status.

A scientific problem solving environment should be built in such a way that users (scientists) might exploit underlying technologies without a specialised knowledge about available tools and resources. An adaptive user interface can be considered as an opportunity in addressing this challenge. This paper explores the importance of individual human abilities in the design of adaptive user interfaces for scientific problem solving environments. In total, seven human factors (gender, learning abilities, locus of control, attention focus, cognitive strategy and verbal and nonverbal IQs) have been evaluated regarding their impact on interface adjustments done manually by users. People’s preferences for different interface configurations have been investigated. The experimental study suggests criteria for the inclusion of human factors into the user model guiding and controlling the adaptation process. To provide automatic means of adaptation, the Intelligent System for User Modelling has been developed.

With the rapid development of information technology, the amount and complexity of scientific data increase exponentially every day.The data explosion has led to very large detailed datasets and the amount of detail in these datasets continues to grow.Nowadays, the challenge is to harness the power of knowledge hidden in the collage of scientific data.Visualisation technologies empower users to perceive important patterns in the data, identify areas that need further scrutiny and make sophisticated decisions.However, looking at information is only the first step in understanding.For some domains, it is unclear what the features of interest are and how to define them in such a way that they can be detected.Many scientific discovery tasks can only be solved in interactive ways: by combining information analysis tools, advanced visualisation techniques and intuitive user interfaces.The importance of human interaction and perception cannot be ignored in the field of scientific computing and data analysis.Interactive exploration systems allow people to examine scientific data and share obtained knowledge with, and through, information technology.These systems should be easy to use and not require sophisticated computer skills, as most of users are rather domain-specific experts than computer scientists.To increase users' abilities to explore the data and better understand the results of experiments based on extensive calculations, new scientific methods need to be developed and applied.This special issue combines papers that discuss how modern visualisation and interaction techniques can augment the information analysis cycle from the user's perspective.The collected manuscripts are updated and expanded versions of papers presented at a series of

This paper is devoted to multi-modal interface design and implementation of a simulated vascular reconstruction system. It provides multi-modal interaction methods such as speech recognition, hand gestures, direct manipulation of virtual 3D objects and measurement tools. The main challenge is that no general interface scenario in existence today can satisfy all the users of the system (radiologists, vascular surgeons, medical students, etc.). The potential users of the system can vary by their skills, expertise level, habits and psycho-motional characteristics. To make a multimodal interface user-friendly is a crucial issue. In this paper we introduce an approach to develop such an efficient, user-friendly multi-modal interaction system. We focus on adaptive interaction as a possible solution to address the variety of end-users. Based on a user model, the adaptive user interface identifies each individual by means of a set of criteria and generates a customized exploration environment.