Volume 11, Issue 1 (Journal of Control, V.11, N.1 Spring 2017)                   JoC 2017, 11(1): 39-50 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Moghadas-Dastjerdi H, Ahmadzadeh M R, Karami M, Ghiasi F, Samani A. A New Optimization Based Method for Estimation and Spatial Localization of Lung’s Air Volume from 3D CT Images. JoC. 2017; 11 (1) :39-50
URL: http://joc.kntu.ac.ir/article-1-384-en.html
1- Isfahan University of Technology
2- Isfahan University of Medical Sciences
3- Western University
Abstract:   (5053 Views)

Lung’s air volume estimation is of great importance in lung disease diagnosis. In this paper a fully automatic algorithm, which we presented recently to estimate the lung’s air volume from CT-images, is more developed. In this algorithm, first a suitable cost function is introduced based on the long parenchyma physics to determine the voxels of lung’s air region. In this paper, a fully automatic framework is proposed to calculate the initial guess for the solution of the optimization problem. Moreover, a 3D model reconstruction technique is utilized to determine spatial localization of the lung’s air region in 3D CT-images. Furthermore, the performance of the whole-lung-volume-based methods and direct lung’s air volume measurment methods are compared and investigated. In order to evaluate the accuracy, porcine’s lung images and clinical human’s lung images from reliable databases are fed to the proposed algorithm. The significant accuracy and robust performance of the proposed algorithm is illustrated with respect to the resolution reduction of CT-images.

Full-Text [PDF 1755 kb]   (2572 Downloads)    
Type of Article: Research paper | Subject: Special
Received: 2016/06/20 | Accepted: 2017/04/19 | ePublished ahead of print: 2017/07/2 | Published: 2017/05/28

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2022 CC BY-NC 4.0 | Journal of Control

Designed & Developed by : Yektaweb