Title
An adaptive rule based automatic lung nodule detection system
Abstract
Automated lung nodule detection through computed tomography (CT) image acquisition is a new and exciting research area of medical image processing. Lung nodules are potentially cancerous growths in the lungs that often appear in CT images as distinct, high intensity spherical objects. We have developed a nodule detection system. The first stage of the nodule detection technique automatically segments the lung regions using a unique 3D region growing approach. The next stage identifies regions of interests (ROIs) by using adaptive multi-level thresholding (MLT) based on the cumulative density function (CDF) of the lung volume. The last stage reduces false positives (FPs) by using unique features such as vessel and lung wall connectivity, a modified bounding box and 3D compaction to compensate for partial volume artifacts due to thick CT slices. We obtain a sensitivity of 80% with approximately 3.05 FPs per slice.
Year
DOI
Venue
2005
10.1007/11552499_85
ICAPR (2)
Keywords
Field
DocType
lung nodule,lung wall connectivity,nodule detection system,lung region,nodule detection technique,automatic lung nodule detection,adaptive rule,ct image,lung volume,next stage,last stage,automated lung nodule detection,computed tomography,cumulant,false positive,partial volume,rule based,region growing,region of interest
Computer vision,Pattern recognition,Lung,Computer science,Image processing,Lung volumes,Artificial intelligence,Region growing,Thresholding,Partial volume,False positive paradox,Minimum bounding box
Conference
Volume
ISSN
ISBN
3687
0302-9743
3-540-28833-3
Citations 
PageRank 
References 
0
0.34
4
Authors
3
Name
Order
Citations
PageRank
Maciej Dajnowiec100.34
Javad Alirezaie210018.42
Paul Babyn316621.42