Ultrasound motion estimation using a hierarchical feature weighting algorithm.
The quality of ultrasonic images is usually influenced by speckle noises and the temporal decorrelation of the speckle patterns. Most traditional motion estimation algorithms are not suitable for speckle tracking in medical ultrasonic images which usually have a low signal-to-noise ratio (SNR). This paper proposes a new motion estimation algorithm that is designed for assessing the dense velocity fields of soft tissue motion in a sequence of ultrasonic B-mode images. We design a hierarchical maximum a posteriori estimator together with an adaptive feature weighted mechanism to estimate the motion field from an ultrasonic image sequence. The proposed method was compared with several existing motion estimation methods via a series of experiments with synthetic speckle image sequences. Performance was also tested on in vivo ultrasonic images. The experimental results show that motion can be assessed with better accuracy than other methods for synthetic speckle images and a good correspondence with clinicians' observations has also been achieved for clinical ultrasonic images.
Lin
Cheng-Hsien
CH
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC.
Lin
Mark Chii-Jeng
MC
Sun
Yung-Nien
YN
D000465:Algorithms; D003238:Connective Tissue / Q000000981:diagnostic imaging*; D007089:Image Enhancement / Q000379:methods; D009068:Movement / Q000502:physiology*; D013624:Taiwan; D014463:Ultrasonography