Interactive Diffusion-based Smoothing and Segmentation of Volumetric Datasets on Graphics Hardware

Summary

Objective : Volume segmentation with concurrent visualization is becoming an increasingly important part of medical diagnostics. This is due to the fact that the immediate visual feedback speeds up evaluation of the segmentation process, hence enhances segmentation quality. Therefore, our aim was to develop a method for volume segmentation and smoothing which achieves interactive performance on standard PCs and is useful in clinical practice (i.e. fast and of high quality).

Methods : Our application is based on seeded region growing and nonlinear isotropic as well as anisotropic diffusion. We use current GPUs (graphics processing units) to speed up the computation of the diffusion process and use hardware-accelerated interactive volume rendering.

Results : Using our approach the user can observe the diffusion process in real-time, change parameters interactively and view the result in a high-quality 3D direct volume rendering (DVR).

Conclusion : The interactive nature of our algorithm and simultaneous visualization improved the usability of our segmentation and smoothing algorithm and proved useful in the clinical workflow. Using our application we were able to speed up the (an)isotropic diffusion process to achieve interactive performance.

Keywords

Volume segmentation - volume smoothing - GPU - diffusion - direct volume rendering

• References

• 1 Weickert J. A Review of Nonlinear Diffusion Filtering. Lecture Notes in Computer Science; Proceedings of the First International Conference on Scale-Space Theory in Computer Vision. 1997: 3-28.
• 2 Suri JS, Wu D, Gao J, Singh S, Laxminarayan S. A Comparison of State-of-the-Art Diffusion Imaging Techniques for Smoothing Medical/Non-Medical Image Data. Proceedings of International Conference on Pattern Recognition (ICPR’02). 2002: 508-517.
• 3 Sherbondy A, Houston M, Napel S. Fast Volume Segmentation With Simultaneous Visualization Using Programmable Graphics Hardware. Proceedings of IEEE Visualization. 2003: 171-176.
• 4 Owens JD, Luebke D, Govindaraju N, Harris M, Krüger J, Lefohn AE. et al. A Survey of General Purpose Computation on Graphics Hardware. Eurographics 2005, State of the Art Reports. 2005: 21-51.
• 5 Rumpf M, Strzodka R. Nonlinear Diffusion in Graphics Hardware. EG/IEEE TCVG Symposium on Visualization Vis Sym. 2001: 75-84.
• 6 Hopf M, Ertl T. Accelerating 3D Convolution Using Graphics Hardware. IEEE Visualization. 1999: 471-474.
• 7 Perona P, Malik J. Scale Space and Edge Detection Using Anisotropic Diffusion. IEEE Transactions in Pattern Analysis and Machine Intelligence 1990; 12 (07) 629-639.
• 8 Hadwiger M, Berger C, Hauser H. High-Quality Two-Level Volume Rendering of Segmented Data Sets on Consumer Graphics Hardware. Proceedings of IEEE Visualization. 2003: 301-308.
• 9 Ghita O, Robinson K, Lynch M, Whelan PF. MRI Diffusion-based Filtering: A Note on Performance Characterisation. Computerized Medical Imaging and Graphics 2005; 29 (04) 267-277.
• 10 Levoy M. Display of Surfaces from Volume Data. IEEE Computer Graphics and Applications 1988; 08: 29-37.
• 11 Rezk-Salama C, Engel K, Bauer M, Greiner G, Ertl T. Interactive Volume Rendering on Standard PC Graphics Hardware Using Multi-Textures and Multi-Stage Rasterization. Proceedings of SIGGRAPH/Eurographics Workshop on Graphics Hardware. 2000: 109-118.
• 12 Krüger J, Westermann R. Acceleration Techniques for GPU-based Volume Rendering. Proceedings of IEEE Visualization. 2003: 287-292.
• 13 Black MJ, Sapiro G, Marimont D, Heeger D. Robust Anisotropic Diffusion. IEEE Transactions on Image Processing 1998; 07: 421-432.
• 14 Scharsach H, Hadwiger M, Neubauer A, Wolfsberger S, Bühler K. Perspective Isosurface and Direct Volume Rendering for Virtual Endoscopy Applications. Eurovis/IEEE-VGTC Symposium on Visualization. 2006: 315-323.
• 15 Chu V, Hamarneh G. MATLAB-ITK Interface for Medical Image Filtering, Segmentation, and Registration. Medical Imaging, Proc. of SPIE. vol. 6144. 2006
• 16 Castellanos J, Rohr K, Tolxdorff T, Wagenknecht G. Automatic Parameter Optimization for Denoising MR Data. Proceedings of MICCAI. 2005: 320-327.