Boundary detection by linear programming with application to lung fields segmentation

Bulat Ibragimov; Boštjan Likar; Franjo Pernuš

doi:10.1117/12.877705

14 March 2011 Boundary detection by linear programming with application to lung fields segmentation

Bulat Ibragimov, Boštjan Likar, Franjo Pernuš

Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 796231 (2011) https://doi.org/10.1117/12.877705
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Medical image segmentation is typically used to locate boundaries of anatomical structures in images acquired by different modalities. As segmentation is of utmost importance for quantitative measurements and analysis of anatomical structures, tracking anatomical changes over time, building anatomical atlases and visualization of medical images, a huge amount of methods have been developed and tested on a wide range of applications in the past. Deformable or parametric shape models are a class of methods that have been widely used for segmentation. A drawback of deformable model approaches it that they require initialization near the final solution. In this paper, we present a segmentation algorithm that incorporates prior knowledge and is composed of two steps. First, reference points on the boundary of an anatomical structure are found by linear programming incorporating prior knowledge. Second, paths between reference points, representing boundary segments, are searched for by optimal control. The segmentation method has been applied to chest radiographs from the publicly available SCR database.

Citation Download Citation

Bulat Ibragimov, Boštjan Likar, and Franjo Pernuš "Boundary detection by linear programming with application to lung fields segmentation", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796231 (14 March 2011); https://doi.org/10.1117/12.877705

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