By Topic

Hybrid lung segmentation in chest 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
$31 $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)
Yeny Yim ; Sch. of Comput. Sci. & Eng., Seoul Nat. Univ., South Korea ; Helen Hong ; Yeong Gil Shin

We propose an automatic segmentation method for accurately identifying lung surfaces in chest CT images. Our method consists of three steps. First, lungs and airways are extracted by an inverse seeded region growing and connected component labeling. Second, trachea and large airways are delineated from the lungs by three-dimensional region growing. Third, accurate lung region borders are obtained by subtracting the result of the second step from that of the first step. The proposed method has been applied to 10 patient datasets with lung cancer or pulmonary embolism. Experimental results show that our segmentation method extracts lung surfaces automatically and accurately. Averaged over all volumes, the root mean square difference between the computer and manual analysis is 1.2 pixels.

Published in:

Enterprise networking and Computing in Healthcare Industry, 2005. HEALTHCOM 2005. Proceedings of 7th International Workshop on

Date of Conference:

23-25 June 2005