Automatic left ventricle segmentation in T2 weighted CMR images

Tools

Kushsairy Bin Abdul Kadir, K and Payne, A. and Soraghan, John and Berry, C.; (2010) Automatic left ventricle segmentation in T2 weighted CMR images. In: Image Processing and Communications Challenges 2. Advances in intelligent and soft computing . Springer, pp. 247-254. ISBN 9783642162954 (https://doi.org/10.1007/978-3-642-16295-4_28)

Full text not available in this repository.Request a copy

Abstract

An automatic left ventricle (LV) segmentation method for T2 weighted Cardiac Magnetic Resonance (CMR) image is presented. The method takes multi-slice T2 weighted CMR images from the basal to the apex of the heart. Inter-slice and intra-slice fuzzy reasoning is used to guide the centre point detection for each slice. Morphological filtering is used in the reconstruction to homogenise the blood pool region. Then radial search Fuzzy Multiscale Edge Detection (FMED) is used to segment the endocardium and the epicardium of the LV. Evaluation of the method is performed on 6 patient with approximately 42 slices of real T2 weighted MRI data. The quantitative result of the automatic method compared to those obtained from manual segmentation by a skilled clinician are very encouraging, with correlation scores of 96.2% correlation for the endocardium and 85.7% correlation for the epicardium.

ORCID iDs

Kushsairy Bin Abdul Kadir, K, Payne, A., Soraghan, John ORCID logoORCID: https://orcid.org/0000-0003-4418-7391 and Berry, C.;
CORE (COnnecting REpositories)