Automatic Segmentation of a Fetal Echocardiogram Using Modified Active Appearance Models and Sparse Representation

A novel approach is presented to automatically segment the left ventricle in fetal echocardiograms. The proposed approach strategically integrates sparse representation, global constraint, and local refinement algorithms into an active appearance model (AAM) framework. In the training stage, we construct an enhanced AAM texture model to deal with the speckle and texture ambiguities. In the segmentation stage, the initial pose is located by a sparse representation method. Globally constrained points and local features with high clinical relevance are effectively incorporated into the AAM framework to make the model converge toward a desired position. Our proposed approach has been compared with the traditional ASM, the traditional AAM, and the globally constrained AAM on the synthetic and clinical data. The results show that compared with experts drawn contours, the overall segmentation accuracy of overlapped area in the synthetic and clinical images are 84.12% and 84.39%, respectively, superior to those of the other three methods. The experiments demonstrate that sparse representative methods greatly facilitate the initializations and our approach is capable of detecting the fetal left ventricle effectively, offering a better segmentation results.