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

Abstract

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). 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. 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). 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.