LV shape recovery from echocardiographic images by means of computer vision techniques and neural networks

The authors describe a fully automated neural-network-based computer-vision system for recovering the endocardial left ventricular surface from a reduced number of echocardiograms. After detecting and linking edges, an edge-segment representation is built for each input image. According to their parameters, edge-segment primitives are then labeled as noise or LV-boundary by a feedforward neural net. The LV-boundary segments are utilized in defining the energy function of a continuous Hopfield neural network. This function is a finite-element approximation of the potential energy of a closed elastic thin-surface in the presence of external forces. The network relaxes into a minimum energy state which represents the reconstructed LV surface. Experimental results are reported which show how this surface description can be used for displaying the static and dynamic LV shape and for measuring parameters such as LV volume