Picture of a black hole

Strathclyde Open Access research that creates ripples...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of research papers by University of Strathclyde researchers, including by Strathclyde physicists involved in observing gravitational waves and black hole mergers as part of the Laser Interferometer Gravitational-Wave Observatory (LIGO) - but also other internationally significant research from the Department of Physics. Discover why Strathclyde's physics research is making ripples...

Strathprints also exposes world leading research from the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

Discover more...

Variational level set method with shape constraint and application to edema cardiac magnetic resonance image

Kadir, K. and Gao, Hao and Payne, A. and Soraghan, J. and Berry, C. (2011) Variational level set method with shape constraint and application to edema cardiac magnetic resonance image. In: Proceedings of the 17th International Conference on Digital Signal Processing (DSP), 2011. IEEE. ISBN 978-1-4577-0273-0

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

Quantification of oedema area after acute myocardial infarction (MI) is very important in clinical prognosis for differentiating the viable and death myocardial tissues. In order to quantify oedema region, the first step is to segment the myocardial wall accurately. This paper applies variational level set method with shape constraint to oedema cardiac magnetic resonance (CMR) images. Shape information of the myocardial wall is introduced into the variational level set formulation, and the performance of the automatic method is tested on T2 weighted CMR images from 8 patients, and compared with manual analysis from two clinical experts. Results show that the proposed automatic segmentation framework can segment left ventricle (LV) boundary with no significant difference compared to manual segmentation.