Ms. Lin Cong Profile

Lin Cong

at United Imaging

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 3 March 2017 Paper

Fully automated calculation of cardiothoracic ratio in digital chest radiographs

Lin Cong, Luan Jiang, Gang Chen, Qiang Li

Proceedings Volume 10134, 1013432 (2017) https://doi.org/10.1117/12.2254398

KEYWORDS: Roads, Lung, Chest imaging, Heart, Image segmentation, Hough transforms, Computer programming, Radiography, Chest, CAD systems, Medicine

Read Abstract +

The calculation of Cardiothoracic Ratio (CTR) in digital chest radiographs would be useful for cardiac anomaly assessment and heart enlargement related disease indication. The purpose of this study was to develop and evaluate a fully automated scheme for calculation of CTR in digital chest radiographs. Our automated method consisted of three steps, i.e., lung region localization, lung segmentation, and CTR calculation. We manually annotated the lung boundary with 84 points in 100 digital chest radiographs, and calculated an average lung model for the subsequent work. Firstly, in order to localize the lung region, generalized Hough transform was employed to identify the upper, lower, and outer boundaries of lung by use of Sobel gradient information. The average lung model was aligned to the localized lung region to obtain the initial lung outline. Secondly, we separately applied dynamic programming method to detect the upper, lower, outer and inner boundaries of lungs, and then linked the four boundaries to segment the lungs. Based on the identified outer boundaries of left lung and right lung, we corrected the center and the declination of the original radiography. Finally, CTR was calculated as a ratio of the transverse diameter of the heart to the internal diameter of the chest, based on the segmented lungs. The preliminary results on 106 digital chest radiographs showed that the proposed method could obtain accurate segmentation of lung based on subjective observation, and achieved sensitivity of 88.9% (40 of 45 abnormalities), and specificity of 100% (i.e. 61 of 61 normal) for the identification of heart enlargements.

Proceedings Article | 29 March 2016 Paper

Segmentation of ribs in digital chest radiographs

Lin Cong, Wei Guo, Qiang Li

Proceedings Volume 9788, 97881T (2016) https://doi.org/10.1117/12.2216742

KEYWORDS: Image segmentation, Chest imaging, Radiography, Hough transforms, Lung, Algorithm development, Computer programming, Medicine, Databases, Image processing algorithms and systems, Chest

Read Abstract +

Ribs and clavicles in posterior-anterior (PA) digital chest radiographs often overlap with lung abnormalities such as nodules, and cause missing of these abnormalities, it is therefore necessary to remove or reduce the ribs in chest radiographs. The purpose of this study was to develop a fully automated algorithm to segment ribs within lung area in digital radiography (DR) for removal of the ribs. The rib segmentation algorithm consists of three steps. Firstly, a radiograph was pre-processed for contrast adjustment and noise removal; second, generalized Hough transform was employed to localize the lower boundary of the ribs. In the third step, a novel bilateral dynamic programming algorithm was used to accurately segment the upper and lower boundaries of ribs simultaneously. The width of the ribs and the smoothness of the rib boundaries were incorporated in the cost function of the bilateral dynamic programming for obtaining consistent results for the upper and lower boundaries. Our database consisted of 93 DR images, including, respectively, 23 and 70 images acquired with a DR system from Shanghai United-Imaging Healthcare Co. and from GE Healthcare Co. The rib localization algorithm achieved a sensitivity of 98.2% with 0.1 false positives per image. The accuracy of the detected ribs was further evaluated subjectively in 3 levels: "1", good; "2", acceptable; "3", poor. The percentages of good, acceptable, and poor segmentation results were 91.1%, 7.2%, and 1.7%, respectively. Our algorithm can obtain good segmentation results for ribs in chest radiography and would be useful for rib reduction in our future study.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years