Robust lung identification in MSCT via controlled flooding and shape constraints: dealing with anatomical and pathological specificity

Catalin Fetita; Sebastian Tarando; Pierre-Yves Brillet; Philippe A. Grenier

doi:10.1117/12.2216687

29 March 2016 Robust lung identification in MSCT via controlled flooding and shape constraints: dealing with anatomical and pathological specificity

Catalin Fetita, Sebastian Tarando, Pierre-Yves Brillet, Philippe A. Grenier

Author Affiliations +

Proceedings Volume 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging; 97881A (2016) https://doi.org/10.1117/12.2216687
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Correct segmentation and labeling of lungs in thorax MSCT is a requirement in pulmonary/respiratory disease analysis as a basis for further processing or direct quantitative measures: lung texture classification, respiratory functional simulations, intrapulmonary vascular remodeling evaluation, detection of pleural effusion or subpleural opacities, are only few clinical applications related to this requirement. Whereas lung segmentation appears trivial for normal anatomo-pathological conditions, the presence of disease may complicate this task for fully-automated algorithms. The challenges come either from regional changes of lung texture opacity or from complex anatomic configurations (e.g., thin septum between lungs making difficult proper lung separation). They make difficult or even impossible the use of classic algorithms based on adaptive thresholding, 3-D connected component analysis and shape regularization. The objective of this work is to provide a robust segmentation approach of the pulmonary field, with individualized labeling of the lungs, able to overcome the mentioned limitations. The proposed approach relies on 3-D mathematical morphology and exploits the concept of controlled relief flooding (to identify contrasted lung areas) together with patient-specific shape properties for peripheral dense tissue detection. Tested on a database of 40 MSCT of pathological lungs, the proposed approach showed correct identification of lung areas with high sensitivity and specificity in locating peripheral dense opacities.

Citation Download Citation

Catalin Fetita, Sebastian Tarando, Pierre-Yves Brillet, and Philippe A. Grenier "Robust lung identification in MSCT via controlled flooding and shape constraints: dealing with anatomical and pathological specificity", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97881A (29 March 2016); https://doi.org/10.1117/12.2216687

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Lung

Image segmentation

Tissues

Neodymium

Spherical lenses

Ions

3D image processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years