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

Content Provider	IEEE Xplore Digital Library
Author	Zhuowen Tu Zhou, X.S. Bogoni, L. Barbu, A. Comaniciu, D.
Copyright Year	2006
Description	Author affiliation: UCLA (Zhuowen Tu)
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.
Starting Page	1544
Ending Page	1551
File Size	800718
Page Count	8
File Format	PDF
ISBN	0769525970
ISSN	10636919
DOI	10.1109/CVPR.2006.228
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computed tomography Colonic polyps Algorithm design and analysis Object detection Convergence Biomedical imaging Virtual colonoscopy Boosting Integral equations Filters
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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