AutoEDES: a model-based Bayesian framework for automatic end-diastolic and end-systolic frame selection in angiographic image sequence

Wei Qu; Sukhveer Singh; Mike Keller

doi:10.1117/12.769693

17 March 2008 AutoEDES: a model-based Bayesian framework for automatic end-diastolic and end-systolic frame selection in angiographic image sequence

Wei Qu, Sukhveer Singh, Mike Keller

Author Affiliations +

Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 69153B (2008) https://doi.org/10.1117/12.769693
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

This paper presents a novel approach to automatically detect the end-diastolic (ED) and end-systolic (ES) frames from an X-ray left ventricular angiographical image sequence. ED and ES image detection is the first step for widely used left ventricular analysis in catheterization lab. However, due to the inherent difficulties of X-ray angiographical image, automatic ED and ES frame selection is a challenging task and still remains unsolved. The current clinical practice uses manual selection, which is not only time consuming but also sensitive to different persons at different time. In this paper, we propose to formulate the X-ray angiogram by a dynamical graphical model. Then the posterior density of the left ventricular state is estimated by using Bayesian probability density propagation and adaptive background modeling. Preliminary experimental results have demonstrated the superior performance of the proposed algorithm on clinical data.

Citation Download Citation

Wei Qu, Sukhveer Singh, and Mike Keller "AutoEDES: a model-based Bayesian framework for automatic end-diastolic and end-systolic frame selection in angiographic image sequence", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69153B (17 March 2008); https://doi.org/10.1117/12.769693

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