Segmentation of cardiac MR images: an active appearance model approach

Active Appearance Models (AAM), which have been recently introduced by Cootes et al., describe the shape of objects and gray level appearance from a set of example images. An AAM is created from user-placed contours defining the shape of objects of interest in each training image. The information about shape changes observed in the training set is used to model the shape variation. Principle component analysis (PCA) is utilized to model gray level variation observed in the training set. The resulting model describes objects as a linear combination of eigen vectors both in shape and gray levels applied to the mean image. The main purpose of this work is to investigate the clinical potential of AAMs for segmentation of cardiovascular MR images acquired in routine clinical practice. An AAM was constructed using 102 end- diastolic short-axis cardiac MR images at the papillary muscle level from normals and patients with varying pathologies. The resulting AAM is a compact representation consisting of a mean image and a limited number of coefficients of eigen vectors, representing 97% of shape and gray level variation observed in the training set. The segmentation performance is tested in 60 end-diastolic short-axis cardiac MR images from different patients.