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 ...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Zhuowen Tu, Xiang Zhou, Dorin Comaniciu, Luca Bogoni
• Keywords: Background Segmentation, Complex Foreground, Computer Vision, Discriminative Model Ratio, Discriminative Models, ECCV 2006, Probabilistic Boosting Tree