Paper Info
Title
Probabilistic 3D Polyp Detection in CT Images: The Role of Sample Alignment
Abstract
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 at each step in t+1. is tightly bounded by the previous error in t. (3) For a 3D polyp template, a generative model is defined. Given the bound and convergence analysis, we analyze the role of "sample alignment" in the template design and devise a robust and efficient algorithm for polyp detection. The overall system has been tested on 150 volumes and the results obtained are very encouraging.
Year
DOI
Venue
2006
10.1109/CVPR.2006.228
CVPR (2)
Keywords
Field
DocType
explicit convergence rate analysis,detection algorithm,ct images,automatic polyp detection,convergence analysis,sample alignment,previous error,efficient algorithm,adaboost algorithm,polyp detection,overall system,polyp template,integral equations,convergence,computed tomography,boosting,biomedical imaging,convergence rate,algorithm design and analysis
Convergence (routing),Computer vision,Object detection,Algorithm design,Pattern recognition,Computer science,Rate of convergence,Boosting (machine learning),Artificial intelligence,Virtual colonoscopy,Probabilistic logic,Generative model
Conference
Volume
ISSN
ISBN
2
1063-6919
0-7695-2597-0
Citations 
PageRank 
References 
45
3.14
12
Authors
5
Name
Order
Citations
PageRank
Zhuowen Tu13663215.79
Xiang Sean Zhou21915129.40
Luca Bogoni361665.11
Adrian Barbu476858.59
Dorin Comaniciu58389601.83