Automatic left ventricle segmentation in T2 weighted CMR images

K Kushsairy Bin Abdul Kadir, A. Payne, John Soraghan, C. Berry

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

3 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationImage Processing and Communications Challenges 2
PublisherSpringer
Pages247-254
Number of pages8
ISBN (Print)9783642162954
DOIs
Publication statusPublished - 2010

Publication series

NameAdvances in intelligent and soft computing
PublisherSpringer
Volume84

Keywords

  • CMR images
  • image processing
  • ventricle segmentation

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    Kushsairy Bin Abdul Kadir, K., Payne, A., Soraghan, J., & Berry, C. (2010). Automatic left ventricle segmentation in T2 weighted CMR images. In Image Processing and Communications Challenges 2 (pp. 247-254). (Advances in intelligent and soft computing; Vol. 84). Springer. https://doi.org/10.1007/978-3-642-16295-4_28