Chapter 13

Medical Image Volumetric Visualization:

Algorithms, Pipelines, and Surgical Applications

Qi Zhang, Terry M. Peters, and Roy Eagleson

13.1 Introduction

With the increasing availability of high-resolution datasets of 3D medical images, the development of volumetric image rendering techniques have become an important complement to classical surface-based rendering. Since volumetric visualization does not require that surfaces be selected from within the 3D volumes, the full volume dataset is maintained during the rendering process. These methods are based on a foundation of projecting rays through volumes, which have a range of opacity attributes, onto a viewing window. Volume rendering is computationally demanding, and the ever increasing size of medical image datasets means that bruteforce algorithms are not feasible for interactive use.

More recently, further efficiencies have been attained by implementing many of these algorithms on graphics processing hardwares (GPUs). In this chapter, we describe volumetric visualization pipelines, and provide a comprehensive overview of rendering algorithms that use effective approximate models to compute volumetric scenes of medical applications. We review and implement several mainstream medical image visualization strategies and rendering pipelines, including multiplanar reformation (MPR), direct and indirect surface rendering (DSR and ISR) with shading, direct volume rendering (DVR), software-based raycasting, 2D and 3D texture mapping (3DTM), GPU-based raycasting, maximum intensity projection (MIP), X-ray based rendering, gradient estimation and different interpolation approaches, voxel classification, and optical composition schemes. We present an overview of these techniques, and also evaluate their image quality and rendering performance.

R. Eagleson ( )

The University of Western Ontario, London, ON, Canada e-mail: [e-mail]

G. Dougherty (ed.), Medical Image Processing: Techniques and Applications, Biological

291

and Medical Physics, Biomedical Engineering, DOI 10.1007/978-1-4419-9779-1 13, © Springer Science+Business Media, LLC 2011

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Where space permits, we have also added some of our recent research results and new rendering and classifications algorithms. In particular, these include anatomical feature enhancement techniques, dynamic multimodality rendering, and interactive manipulation. We have implemented a GPU-based medical image manipulation and visualization system with these volume rendering enhancements. We compare the performance of our strategies with those obtained by implementation algorithms from the published literature. We also address the advantages and drawbacks of each in terms of image quality and speed of interaction.

Fig. 13.1 MPR of 3D cardiac CT image. Top: three arbitrary cross-planes. Bottom: synchronized 2D displays of cross-planes