Paper Info
Title
Automated Lung Segmentation And Computer-Aided Diagnosis For Thoracic Ct Scans
Abstract
Automated segmentation of the lungs in thoracic computed tomography (CT) scans represents an essential process in the development of computer-aided diagnostic (CAD) methods and computer-assisted quantification techniques. A core segmentation process may be developed for general application; however, modifications may be required for specific clinical tasks. We have developed such an automated lung segmentation method based on gray-level thresholding techniques and have applied this method (1) as a pre-processing step for automated lung nodule detection and (2) as the foundation for a computer-assisted technique to measure the extent of pleural mesothelioma. In the automated detection of lung nodules, we have developed a method that has achieved 71% nodule detection sensitivity with an average of 0.4 false-positive detections per section on a database of 38 CT scans. Our method for the computer-assisted quantification of mesothelioma achieved a correlation coefficient of 0.97 with the average manual measurements of four observers based on 134 measurement sites in 22 CT scans. Important differences exist in the specific approaches to automated lung segmentation required for these two clinical tasks. (C) 2003 Published by Elsevier Science B.V.
Year
DOI
Venue
2003
10.1016/S0531-5131(03)00388-1
CARS 2003: COMPUTER ASSISTED RADIOLOGY AND SURGERY, PROCEEDINGS
Keywords
Field
DocType
computer-aided diagnosis (CAD), image processing, computed tomography, image segmentation
CAD,Computer vision,Segmentation,Computer-aided diagnosis,Image processing,Image segmentation,Mesothelioma,Artificial intelligence,Thresholding,Radiology,Medicine,Lung segmentation
Conference
Volume
ISSN
Citations 
1256
0531-5131
10
PageRank 
References 
Authors
0.91
7
2
Name
Order
Citations
PageRank
Samuel G. Armato III1869.72
Heber MacMahon220231.61