By Topic

Autonomous detection of solitary pulmonary nodules on CT images for computer-aided diagnosis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Wei Ying ; College of Information Science and Engineering, Northeastern University, and Key Laboratory of Medical Image Computing Ministry of Education Shenyang, China ; Jia Tong ; Lin Ming-xiu

In this paper, algorithms of ROI segmentation, feature selecting and classifying were studied, and a novel scheme has been proposed to detect solitary pulmonary nodules on CT images. ROIs are segmented based on multi-scale morphological filtering method, features of ROI are selected using separability of probability, and ROIs are classified to nodule or non-nodule by improved Mahalanobis distance. Twenty clinical cases were tested in this study, the sensitivity of nodule detection is 94.6%. Experiment results indicated that lung nodule detection using the proposed algorithms is with high sensitivity and low false positive rate, it can provide helpful information for automatic detection of pulmonary nodules in a computer-aided diagnosis(CAD) system.

Published in:

2011 Chinese Control and Decision Conference (CCDC)

Date of Conference:

23-25 May 2011