A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection Using Markov Random Fields
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
RD-based seeded region growing for extraction of breast tumor in an ultrasound volume
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
| Hi-index | 0.00 |
In this paper we address the problem of ultrasound volume data segmentation by means of active contours. The segmentation is performed on an slice-by-slice basis but exploiting the high correlation that exists across nearby slices. We have used active rays to alleviate the computational load of the optimization process. Classical energy functions have trouble finding the optimal solution in speckle imagery. We therefore propose new energy functions that work well under this type of images. Moreover, we propose both a probabilistic relaxation scheme and a heuristic search of the minimal energy configuration to end up making a hybrid use of both. We compare two rendered surfaces to illustrate the smoothness of our reconstruction.