  Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) can directly
visualize nonviable myocardium with hyperenhanced intensities with respect to
normal myocardium. For heart attack patients, it is crucial to facilitate the
decision of appropriate therapy by analyzing and quantifying their LGE CMR
images. To achieve accurate quantification, LGE CMR images need to be processed
in two steps: segmentation of the myocardium followed by classification of
infarcts within the segmented myocardium. However, automatic segmentation is
difficult usually due to the intensity heterogeneity of the myocardium and
intensity similarity between the infarcts and blood pool. Besides, the slices
of an LGE CMR dataset often suffer from spatial and intensity distortions,
causing further difficulties in segmentation and classification. In this paper,
we present a comprehensive 3-D framework for automatic quantification of LGE
CMR images. In this framework, myocardium is segmented with a novel method that
deforms coupled endocardial and epicardial meshes and combines information in
both short- and long-axis slices, while infarcts are classified with a
graph-cut algorithm incorporating intensity and spatial information. Moreover,
both spatial and intensity distortions are effectively corrected with specially
designed countermeasures. Experiments with 20 sets of real patient data show
visually good segmentation and classification results that are quantitatively
in strong agreement with those manually obtained by experts.
