Share Email Print

Proceedings Paper

AutoEDES: a model-based Bayesian framework for automatic end-diastolic and end-systolic frame selection in angiographic image sequence
Author(s): Wei Qu; Sukhveer Singh; Mike Keller
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 17 March 2008
PDF: 8 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69153B (17 March 2008); doi: 10.1117/12.769693
Show Author Affiliations
Wei Qu, Siemens Medical Solutions USA, Inc. (United States)
Sukhveer Singh, Siemens Medical Solutions USA, Inc. (United States)
Mike Keller, Siemens Medical Solutions USA, Inc. (United States)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

© SPIE. Terms of Use
Back to Top