Airway extraction from 3D chest CT volumes based on iterative extension of VOI enhanced by cavity enhancement filter

Qier Meng; Takayuki Kitasaka; Masahiro Oda; Kensaku Mori

doi:10.1117/12.2254233

3 March 2017 Airway extraction from 3D chest CT volumes based on iterative extension of VOI enhanced by cavity enhancement filter

Qier Meng, Takayuki Kitasaka, Masahiro Oda, Kensaku Mori

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101343S (2017) https://doi.org/10.1117/12.2254233
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Airway segmentation is an important step in analyzing chest CT volumes for computerized lung cancer detection, emphysema diagnosis, asthma diagnosis, and pre- and intra-operative bronchoscope navigation. However, obtaining an integrated 3-D airway tree structure from a CT volume is a quite challenging task. This paper presents a novel airway segmentation method based on intensity structure analysis and bronchi shape structure analysis in volume of interest (VOI). This method segments the bronchial regions by applying the cavity enhancement filter (CEF) to trace the bronchial tree structure from the trachea. It uses the CEF in each VOI to segment each branch and to predict the positions of VOIs which envelope the bronchial regions in next level. At the same time, a leakage detection is performed to avoid the leakage by analysing the pixel information and the shape information of airway candidate regions extracted in the VOI. Bronchial regions are finally obtained by unifying the extracted airway regions. The experiments results showed that the proposed method can extract most of the bronchial region in each VOI and led good results of the airway segmentation.

Citation Download Citation

Qier Meng, Takayuki Kitasaka, Masahiro Oda, and Kensaku Mori "Airway extraction from 3D chest CT volumes based on iterative extension of VOI enhanced by cavity enhancement filter", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343S (3 March 2017); https://doi.org/10.1117/12.2254233

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available