Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
Multilabel Random Walker Image Segmentation Using Prior Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive segmentation of image volumes with Live Surface
Computers and Graphics
IEEE Transactions on Image Processing
Object information based interactive segmentation for fatty tissue extraction
Computers in Biology and Medicine
Hi-index | 0.00 |
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 expert's 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 Dice's similarity coefficient of 0.975, yet our method runs in 1/25th of the time.