Probabilistic 3D Polyp Detection in CT Images: The Role of Sample Alignment

Automatic polyp detection is an increasingly important task in medical imaging with virtual colonoscopy [15] being widely used. In this paper, we present a 3D object detection algorithm and show its application on polyp detection from CT images. We make the following contributions: (1) The system adopts Probabilistic Boosting Tree (PBT) to probabilistically detect polyps. Integral volume and 3D Haar filters are introduced to achieve fast feature computation. (2) We give an explicit convergence rate analysis for the AdaBoost algorithm [2] and prove that the error

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Zhuowen Tu, Xiang Sean Zhou, Luca Bogoni, Adrian Barbu, Dorin Comaniciu
• Keywords: Adaboost Algorithm, Automatic Polyp Detection, Computer Vision, CVPR 2006, Explicit Convergence Rate, Object Detection Algorithm, Probabilistic Boosting Tree