Texture analysis for infarcted myocardium detection on delayed enhancement MRI

Detection of infarcted myocardium in the left ventricle is achieved with delayed enhancement magnetic resonance imaging (DE-MRI). However, manual segmentation is tedious and prone to variability. We studied three texture analysis methods (run-length matrix, co-occurrence matrix, and autoregressive model) in combination with histogram features to characterize the infarcted myocardium. We evaluated 10 patients with chronic infarction to select the most discriminative features and to train a support vector machine (SVM) classifier. The classifier model was then used to segment five human hearts from the STACOM DE-MRI challenge at MICCAI 2012. The Dice coefficient was used to compare the segmentation results with the ground truth available in the STACOM dataset. Segmentation using texture features provided good results with an overall Dice coefficient of 0.71 ± 0.12 (mean ± standard deviation).