NDLI: Detection and recognition of lung abnormalities using deformable templates

Content Provider	IEEE Xplore Digital Library
Author	Farag, A.A. El-Baz, A. Gimel'farb, G. Falk, R.
Copyright Year	2004
Description	Author affiliation: CVIP Lab., Louisville Univ., KY, USA (Farag, A.A.; El-Baz, A.)
Abstract	Automatic detection and recognition of lung cancer during mass screening of spiral computer tomographic (CT) chest scans is one of the most important problems of today's medical image analysis. We propose an algorithm for isolating lung abnormalities (nodules) from arteries, veins, bronchi, and bronchioles after all these objects have been already separated from the surrounding anatomical structures. The separation is presented elsewhere, and this paper focuses on nodule detection using deformable 3D and 2D templates describing typical geometry and gray level distribution within the nodules of the same type. The detection combines normalized cross-correlation template matching by genetic optimization and Bayesian post-classification. Experiments with 200 spiral low dose CT (LDCT) scans confirm the accuracy of our approach.
Starting Page	738
Ending Page	741
File Size	486433
Page Count	4
File Format	PDF
ISBN	0769521282
ISSN	10514651
DOI	10.1109/ICPR.2004.1334634
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lungs Spirals Computed tomography Cancer detection Image recognition Biomedical imaging Image analysis Arteries Veins Respiratory system
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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