ABSTRACT
A novel way to incorporate temporal information with level set algorithm is proposed to counter the dropout problem when detecting ventricular contours in echocardiographic raphic image sequences. The temporal information ided embed- ed into the speed term of the level set equation. By identifying the ventricular contours as strong or weak segments, the weak segments are strengthened based on temporal information from neighboring frames. Hence disrupted heart wall boundary structure information due to dropout can be recovered. A Gaussian Mixture Model (GMM) is employed to compute thresholds separating the segments. A weight and a strengthening ng factor are used to control the information recovery process. Experimental results show the proposed method exhibits good performance when tracking the ventricular boundary in real echocardiographic data.
ABSTRACT
A novel way to incorporate temporal information with level set algorithm is proposed to counter the dropout problem when detecting ventricular contours in echocardiographic raphic image sequences. The temporal information ided embed- ed into the speed term of the level set equation. By identifying the ventricular contours as strong or weak segments, the weak segments are strengthened based on temporal information from neighboring frames. Hence disrupted heart wall boundary structure information due to dropout can be recovered. A Gaussian Mixture Model (GMM) is employed to compute thresholds separating the segments. A weight and a strengthening ng factor are used to control the information recovery process. Experimental results show the proposed method exhibits good performance when tracking the ventricular boundary in real echocardiographic data.
