Abstract.
Foreground and background segmentation is a typical prob- lem 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 probabilis- tic boosting tree [9] for learning discriminative models for the appearance of complex foreground and background. The discriminative model ra- tio 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.