Fast Random Walker with Priors Using Precomputation for Interactive Medical Image Segmentation
Updating segmentation results in real-time based on repeated user input is a reliable way to guarantee accuracy, paramount in medical imaging applications, while making efficient use of an experts time. The random walker algorithm with priors is a robust method able to find a globally optimal probabilistic segmentation with an intuitive method for user input. However, like many other segmentation algorithms, it can be too slow for real-time user interaction. We propose a speedup to this popular algorithm based on offline precomputation, taking advantage of the time images are stored on servers prior to an analysis session. Our results demonstrate the benefits of our approach. For example, the segmentations found by the original random walker and by our new precomputation method for a given 3D image have a Dices similarity coefficient of 0.975, yet our method runs in 1/25th of the time.
Shawn Andrews Ghassan Hamarneh Ahmed Saad
Medical Image Analysis Lab, Simon Fraser University, Canada
国际会议
北京
英文
9–16
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）