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SU‐G‐JeP1‐03: Automatic Motion Tracking Reset in Ultrasound Liver Image Sequences
Author(s) -
Xu Kele,
W Ruixing,
Zhu Li,
Liu Chang,
Zhao Yi
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4956978
Subject(s) - initialization , artificial intelligence , computer vision , computer science , tracking (education) , similarity (geometry) , robustness (evolution) , feature (linguistics) , pattern recognition (psychology) , image (mathematics) , psychology , pedagogy , biochemistry , chemistry , linguistics , philosophy , gene , programming language
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 landmarks 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.