Automatic detection of myocardial infarction through a global shape feature based on local statistical modeling

Abstract

This paper presents a local-to-global statistical approach for modeling the major components of left ventricular (LV) shape using its 3-D landmark representation. The rationale for dividing the LV into local areas is bi-fold: (1) to better identify abnormalities that lead to local shape remodeling and, (2) to decrease the number of shape variables by using a limited set of landmark points for an efficient statistical parametrization. Principal Component Analysis (PCA) is used for the statistical modeling of the local regions and subsets of the learned parameters that provide significant discriminatory information are taken from each local model in a feature selection stage. The selected local parameters are then concatenated to form a global representation of the LV and to train a classifier for differentiating between normal and infarcted LV shapes.