Dictionary learning techniques for left ventricle (LV) analysis and fibrosis detection in cardiac magnetic resonance imaging (MRI)

The characterization of cardiac function is of high clinical interest for early diagnosis and better patient follow-up in cardiovascular diseases. A large number of cardiac image analysis methods and more precisely in cine-magnetic resonance imaging (MRI) have been proposed to quantify both shape and motion parameters. However, the first major problem to address lies in the cardiac image segmentation that is most often needed to extract the myocardium before any other process such as motion tracking, or registration. Moreover, intelligent systems based on classification and learning techniques have emerged over the last years in medical imaging. In this chapter we focus in the use of sparse representation and dictionary learning (DL) in order to get insights about the diseased heart in the context of cardiovascular diseases (CVDs). Specifically, this work focuses on fibrosis detection in patients with hypertrophic cardiomyopathy (HCM).