Cavity boundary detection from sequential echocardiograms using a temporally adaptive multilevel energy function

The floating, 1-D, cyclic Markov random field (F1DCMRF) energy function (EF) based optimization technique is designed and implemented on a time sequence of echocardiograms to perform cavity boundary detection. Temporal information from the sequence is utilized intelligently through an adaptive multilevel energy function. The weight assigned to the temporal continuity component of the EF is allowed to increase as the correlation between the F1DCMRF configuration at time t, R_t, and the convergence configuration at time t-1, R_t-1^conv, improves. This allows for a high temporal weight in sequence images that have a high degree of similarity and a low weight in those that do not. Using a F1DCMRF eliminates ad hoc preliminary boundary location estimation; thus, large errors which could have been introduced due to preprocessing are avoided at very little additional computational cost