Segmentation of scarred and non-scarred myocardium in LG enhanced CMR images using intensity-based textural analysis

• Published in: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2011; pp. 5698 - 5701
• Main Authors: Kotu, L. P., Engan, K., Eftestol, T., Orn, S., Woie, L.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01-01-2011
• Subjects: Algorithms; Contrast Media; Dictionaries; Gadolinium; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image segmentation; Magnetic Resonance Imaging, Cine - methods; Maximum likelihood estimation; Myocardial Stunning - pathology; Myocardium; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity; Sensitivity and Specificity; Training; Vectors
• DOI: 10.1109/IEMBS.2011.6091379
• EISSN: 1558-4615
• ISBN: 9781424441211; 1424441218
• ISSN: 1094-687X; 1557-170X

The Late Gadolinium (LG) enhancement in Cardiac Magnetic Resonance (CMR) imaging is used to increase the intensity of scarred area in myocardium for thorough examination. Automatic segmentation of scar is important because scar size is largely responsible in changing the size, shape and functioning of left ventricle and it is a preliminary step required in exploring the information present in scar. We have proposed a new technique to segment scar (infarct region) from non-scarred myocardium using intensity-based texture analysis. Our new technique uses dictionary-based texture features and dc-values to segment scarred and non-scarred myocardium using Maximum Likelihood Estimator (MLE) based Bayes classification. Texture analysis aided with intensity values gives better segmentation of scar from myocardium with high sensitivity and specificity values in comparison to manual segmentation by expert cardiologists.