Cluster-Based Multi-Dimensional Visualization

There is a growing need for high-frame-rate low-latency visualization solutions as medical practice moves toward interventional procedures. We present a cost-effective visualization system well suited for off-line visualization and interventional procedures. Users can view large time-resolved multi-dimensional datasets in real time with GPU cluster visualization. In addition, computational pre-processing can be hidden by rendering across distributed graphics cards, leading to improved frame-rates over a single graphics card solution. Finally, rendering on graphics cards offloads CPU cycles for generating the next time frame in the visualization. We have developed a network arbitration protocol for GPU cluster visualization called “token scheduling.” Our protocol reduces communication latency, which in turn lowers visualization latency and improves system stability and scalability. In addition, we evaluate GPU cluster behavior and performance through a timing analysis. This analysis leads to a better understanding of cluster size needed to achieve the desired frame rate of a given problem.

Individual Differences Among Users

This chapter examines human factors associated with using interactive three-dimensional (3D) visualizations. Virtual representations of anatomical structure and function, often with sophisticated user control capabilities, are growing in popularity in medicine for education, training, and simulation. This chapter reviews the cognitive science literature and introduces issues such as theoretical ideas related to using interactive visualizations, different types and levels of interactivity, effects of different kinds of control interfaces, and potential cognitive benefits of these tools. The authors raise the question of whether all individuals are equally capable of using 3D visualizations effectively, focusing particularly on two variables: (1) individual differences in spatial abilities, and (2) individual differences in interactive behavior. The chapter draws together findings from the authors’ own studies and from the wider literature, exploring recent insights into how individual differences among users can impact the effectiveness of different types of external visualizations for different kinds of tasks. The chapter offers recommendations for design, such as providing transparent affordances to support users’ meta-cognitive understanding, and employing personalization to complement the capabilities of different individuals. Finally, the authors suggest future directions and approaches for research, including the use of methodology such as needs analysis and contextual enquiry to better understand the cognitive processes and capacities of different kinds of users.

Methods and Applications for Segmenting 3D Medical Image Data

In this chapter, we will give an intuitive introduction to the general problem of 3D medical image segmentation. We will give an overview of the popular and relevant methods that may be applicable, with a discussion about their advantages and limits. Specifically, we will discuss the issue of incorporating prior knowledge into the segmentation of anatomic structures and describe in detail the concept and issues of knowledge-based segmentation. Typical sample applications will accompany the discussions throughout this chapter. We hope this will help an application developer to improve insights in the understanding and application of various computer vision approaches to solve real-world problems of medical image segmentation.

Graphical Modeling of Human Muscles

Muscle simulation is an important component of human modeling. However, there have been few attempts to demonstrate, in an anatomically-correct way, muscle structures and the way in which these change during motion. This chapter proposes a feature-based approach to muscle modeling which attempts to provide models for human musculature based on the real anatomical structures. These models provide a good visual description and form a sound basis for further developments towards medically-accurate simulation of human bodies. Three major problems have been addressed: geometric modeling, deformation and texture. To allow for the wide variety of muscle shapes encountered in the body, the geometric models are based on muscle features identified from radiological data. The results are realistic models with correct anatomical structures, the deformation of these muscle models is fully controlled by, and consistent with, the motion of underlying joint. We suggest a general deformation model that can be adopted for many of our muscle models, but we also model separately the deformation of specific cases for which the general model is not suitable. Interactions between muscles are also taken into account to avoid penetration occurring between adjacent muscles in our model. To provide a suitable visual effect, an algorithm was developed to generate the muscle texture directly on the model surface, rather than by using conventional pattern mapping on to the surface. Some results are presented on the geometric modeling, the deformation and the texture of muscles related to the knee.

Biomedical Image Registration for Diagnostic Decision Making and Treatment Monitoring

The chapter introduces biomedical image registration as a means of integrating and providing complementary and additional information from multiple medical images simultaneously to facilitate diagnostic decision-making and treatment monitoring. It focuses on the fundamental theories of biomedical image registration, major methodologies and contributions of this area, and the main applications of biomedical image registration in clinical contexts. Furthermore, discussions on the future challenges and possible research trends of this field are presented. The chapter aims to assist in a quick understanding of main methods and technologies, current issues, and major applications of biomedical image registration, to provide the connection between biomedical image registration and the related research areas, and finally to evoke novel and practical registration methods to improve the quality and safety of healthcare.

Parallel Segmentation of Multi-Channel Images Using Multi-Dimensional Mathematical Morphology

Multi-channel images are characteristic of certain applications, such as medical imaging or remotely sensed data analysis. Mathematical morphology-based segmentation of multi-channel imagery has not been fully accomplished yet, mainly due to the lack of vector-based strategies to extend classic morphological operations to multidimensional imagery. For instance, the most important morphological approach for image segmentation is the watershed transformation, a hybrid of seeded region growing and edge detection. In this chapter, we describe a vector-preserving framework to extend morphological operations to multi-channel images, and further propose a fully automatic multi-channel watershed segmentation algorithm that naturally combines spatial and spectral/temporal information. Due to the large data volumes often associated with multi-channel imaging, this chapter also develops a parallel implementation strategy to speed up performance. The proposed parallel algorithm is evaluated using magnetic resonance images and remotely sensed hyperspectral scenes collected by the NASA Jet Propulsion Laboratory Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).

Image Segmentation

This chapter introduces image segment techniques. These techniques, including pixel- based approaches, region-based approaches, classification techniques, deformable model algorithms, artificial neural network approaches and texture-based algorithms are detailed. Color image segmentations and 3D image segment methods are briefly introduced. With developments of computer and medical imaging techniques, there is an increase in demand of new segment algorithms for developing or upgrading medical image systems. Therefore, the author hopes that this chapter not only details the current segment techniques, but also assists researchers in quickly selecting their research directions under their applications, imaging modality, image features and other factors.

Automated Overlay of Infrared and Visual Medical Images

Medical infrared imaging captures the temperature distribution of the human skin and is employed in various medical applications. Often it is useful to cross-reference the resulting thermograms with visual images of the patient, either to see which part of the anatomy is affected by a certain disease or to judge the efficacy of the treatment. In this chapter, we show that image registration techniques can be effectively used to generate an overlay of visual and thermal images and provide a useful diagnostic visualisation for the clinician.

Reporting Clinical Gait Analysis Data

Gait analysis is a special investigation that can assist clinical staff in the decision making process regarding treatment options for patients with walking difficulties. Interpretation of gait analysis data recorded from 3D motion capture systems is a time consuming and complex process. This chapter describes techniques and a software program that can be used to simplify interpretation of gait data. It can be viewed with an interactive display and a gait report can be produced more quickly with the key results highlighted. This will allow referring clinicians to integrate the relevant gait measurements and observations and to formulate the patient treatment plan. Although an abbreviated analysis may be useful for clinicians, a full explanation with the key features highlighted is helpful for movement scientists. Visualization software has been developed that directs the clinician and scientist to the relevant parts of the data simplifying the analysis and increasing insight.

Grid-Based Visualization and its Medical Applications

Traditional visualization approaches cannot handle new challenges in the visualization field such as visualizing huge data sets, communicating between existing visualization systems and providing interactive visualization services, widely. In this chapter, the authors introduce an emerging research direction in the visualization field, grid-based visualization, which aims to resolves the above problems by utilizing grid computing technology. However, current grid computing technology is almost batch job-oriented and does not support interactive visualization applications natively. In this chapter, the authors implement a grid-based visualization system (GVis) which utilizes large-scale computing resources to achieve large dataset visualization in real time and provides end users with reliable interactive visualization services, widely. In GVis system, current grid computing technology is extended to support interactive visualization applications.