A Learning Based Approach for 3D Segmentation and Colon Detagging
Abstract
Foreground and background segmentation is a typical problem in computer vision and medical imaging. In this paper, we propose a new learning based approach for 3D segmentation, and we show its application on colon detagging. In many problems in vision, both the foreground and the background observe large intra-class variation and inter-class similarity. This makes the task of modeling and segregation of the foreground and the background very hard. The framework presented in this paper has the following key components: (1) We adopt probabilistic boosting tree [9] for learning discriminative models for the appearance of complex foreground and background. The discriminative model ratio is proved to be a pseudo-likelihood ratio modeling the appearances. (2) Integral volume and a set of 3D Haar filters are used to achieve efficient computation. (3) We devise a 3D topology representation, grid-line , to perform fast boundary evolution. The proposed algorithm has been tested on over 100 volumes of size 500 × 512 × 512 at the speed of 2 ~ 3 minutes per volume. The results obtained are encouraging.
Cite
Text
Tu et al. "A Learning Based Approach for 3D Segmentation and Colon Detagging." European Conference on Computer Vision, 2006. doi:10.1007/11744078_34Markdown
[Tu et al. "A Learning Based Approach for 3D Segmentation and Colon Detagging." European Conference on Computer Vision, 2006.](https://mlanthology.org/eccv/2006/tu2006eccv-learning/) doi:10.1007/11744078_34BibTeX
@inproceedings{tu2006eccv-learning,
title = {{A Learning Based Approach for 3D Segmentation and Colon Detagging}},
author = {Tu, Zhuowen and Zhou, Xiang Sean and Comaniciu, Dorin and Bogoni, Luca},
booktitle = {European Conference on Computer Vision},
year = {2006},
pages = {436-448},
doi = {10.1007/11744078_34},
url = {https://mlanthology.org/eccv/2006/tu2006eccv-learning/}
}