  We present a fully-automated segmentation and quantification of the left
atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target
late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from
the same acquisition session. We formulate the joint distribution of images
using a multivariate mixture model (MvMM), and employ the maximum likelihood
estimator (MLE) for texture classification of the images simultaneously. The
MvMM can also embed transformations assigned to the images to correct the
misregistration. The iterated conditional mode algorithm is adopted for
optimization. This method first extracts the anatomical shape of the LA, and
then estimates a prior probability map. It projects the resulting segmentation
onto the LA surface, for quantification and analysis of scarring. We applied
the proposed method to 36 clinical data sets and obtained promising results
(Accuracy: $0.809\pm .150$, Dice: $0.556\pm.187$). We compared the method with
the conventional algorithms and showed an evidently and statistically better
performance ($p<0.03$).
