Program Information
Automatic Motion Tracking Reset in Ultrasound Liver Image Sequences
Kele XU1 , Ruixing WANG2 , Li ZHU3 , Chang LIU4 , Yi ZHAO5*, (1) College of Eletronical Science and Engineering, National University of Defense Technology, Changsha, Hunan, (2) College of Optoelectronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, (3) Institute of Electrostatic and Electromagnetic Protection, Mechanical Engineering College, Shijiazhuang, Hebei, (4) Sun Yat-Sen University, Guangzhou, Guangdong, (5) IPAG, Paris, Ile De France
Presentations
SU-G-JeP1-3 (Sunday, July 31, 2016) 4:00 PM - 4:30 PM Room: ePoster Theater
Purpose:
We explore an automatic anatomical landmarks tracking reset method in the ultrasound liver sequences, with the goal to improve the robustness and accuracy of the tracking.
Methods:
The proposed tracking failure re-initialization method works as follows: before anatomic land-marks tracking is carried out, the image similarity coefficient is calculated (In our method, the complex wavelet structural similarity is used), between the region of interest (ROI) selected in current frame and the ROI in the first frame. If this similarity coefficient exceeds a set threshold, the positions of anatomical landmarks are reset to those which were input for the first frame. This provides a method to prevent accumulation of errors over long sequences, which can lead to erroneous tracking, and amounts to a sort of “automatic reset” of the tracking starting points based on initial a priori information.
Results:
The experiments are conducted to track the landmarks in the 5 volunteer B-mode ultrasound liver sequences. The results were evaluated by comparison with manual annotations of liver feature (vessels) throughout each image sequence which were provided after automated tracking was complete. Tracking accuracy was evaluated using the Euclidean distance between tracked points and manually annotated points which was summarized by the mean. The mean error for all ROIs in different sequences are about 1-1.5 mm with the proposed automatic reset method, while the error is about 2 mm without the automatic reset method.
Conclusion:
Due to the periodic motion of the anatomical landmarks, the proposed image similarity-based automatic re-initialization method can improve the accuracy and robustness of the motion tracking in the ultrasound liver sequences. It may also be applied to other organs’ tracking problem using ultrasound imaging.
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