Incorporating Temporal Information Into Level Set Functional for Robust Ventricular Boundary Detection From Echocardiographic Image Sequence

Echocardiographic images often suffer from dropouts that lead to loss of signals on the ventricular boundary and cause the level set curve used to detect the boundary leaking out from the gaps on the boundary. In this paper, a novel method that incorporates temporal information into the level set functional is proposed to solve the leakage problem encountered when detecting the heart wall boundary from the echocardiographic image sequence. The ventricular boundary is quantitatively partitioned and classified into strong and weak segments. The weak segments are considered to be weakened by dropouts and there is low confidence on the presence of boundary. Temporal information from neighboring frames is exploited as a regularizer into the level set equation. Hence, the original boundary information in the weak segments can be reconstructed and the curve leakage problem can be remedied. Experimental results demonstrate the advantages of the proposed method for the intended task.