Dr. Timo Kohlberger Profile

Dr. Timo Kohlberger

at Siemens Corp Corporate Technology

SPIE Involvement:

Author

Publications (1)

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 | 2 March 2011 Paper

Advanced level set segmentation of the right atrium in MR

Siqi Chen, Timo Kohlberger, Klaus Kirchberg

Proceedings Volume 7964, 796431 (2011) https://doi.org/10.1117/12.877728

KEYWORDS: Image segmentation, Heart, 3D modeling, Principal component analysis, Blood, Atrial fibrillation, Scalable video coding, Image processing algorithms and systems, Medical imaging, Magnetism

Read Abstract +

Atrial fibrillation is a common heart arrhythmia, and can be effectively treated with ablation. Ablation planning requires 3D models of the patient's left atrium (LA) and/or right atrium (RA), therefore an automatic segmentation procedure to retrieve these models is desirable. In this study, we investigate the use of advanced level set segmentation approaches to automatically segment RA in magnetic resonance angiographic (MRA) volume images. Low contrast to noise ratio makes the boundary between the RA and the nearby structures nearly indistinguishable. Therefore, pure data driven segmentation approaches such as watershed and ChanVese methods are bound to fail. Incorporating training shapes through PCA modeling to constrain the segmentation is one popular solution, and is also used in our segmentation framework. The shape parameters from PCA are optimized with a global histogram based energy model. However, since the shape parameters span a much smaller space, it can not capture fine details of the shape. Therefore, we employ a second refinement step after the shape based segmentation stage, which follows closely the recent work of localized appearance model based techniques. The local appearance model is established through a robust point tracking mechanism and is learned through landmarks embedded on the surface of training shapes. The key contribution of our work is the combination of a statistical shape prior and a localized appearance prior for level set segmentation of the right atrium from MRA. We test this two step segmentation framework on porcine RA to verify the algorithm.

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