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Automatic extraction of abnormal regions from lung images
conference contribution
posted on 2009-01-01, 00:00 authored by Alycia LeeAlycia Lee, Abbas KouzaniAbbas KouzaniA system that could automatically extract abnormal lung regions may assist expert radiologists in verifying lung tissue abnormalities. This paper presents an automated lung nodule detection system consisting of five components: acquisition, pre-processing, background removal, detection, and false positives reduction. The system employs a combination of an ensemble classification and clustering methods. The performance of the developed system is compared against some existing counterparts. Based 011 the experimental results, the proposed system achieved a sensitivity of 100% and a false-positives/slice of 0.67 for 30 tested CT images.
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Event
Bioelectronics and Bioinformatics. Symposium (2009 : Melbourme, Victoria)Pagination
80 - 83Publisher
ISBBLocation
Melbourne, VictoriaPlace of publication
Melbourne, Vic.Start date
2009-12-09End date
2009-12-11Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2009, ISBBTitle of proceedings
ISBB 2009 : Proceedings of the 2009 International Symposium on Bioelectronics and BioinformaticsUsage metrics
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