DocumentCode :
1079142
Title :
Real-Time Vessel Segmentation and Tracking for Ultrasound Imaging Applications
Author :
Guerrero, Julian ; Salcudean, Septimiu E. ; Mcewen, James A. ; Masri, Bassam A. ; Nicolaou, Savvakis
Author_Institution :
Univ. of British Columbia, Vancouver
Volume :
26
Issue :
8
fYear :
2007
Firstpage :
1079
Lastpage :
1090
Abstract :
A method for vessel segmentation and tracking in ultrasound images using Kalman filters is presented. A modified Star-Kalman algorithm is used to determine vessel contours and ellipse parameters using an extended Kalman filter with an elliptical model. The parameters can be used to easily calculate the transverse vessel area which is of clinical use. A temporal Kalman filter is used for tracking the vessel center over several frames, using location measurements from a handheld sensorized ultrasound probe. The segmentation and tracking have been implemented in real-time and validated using simulated ultrasound data with known features and real data, for which expert segmentation was performed. Results indicate that mean errors between segmented contours and expert tracings are on the order of 1%-2% of the maximum feature dimension, and that the transverse cross-sectional vessel area as computed from estimated ellipse parameters a, b as determined by our algorithm is within 10% of that determined by experts. The location of the vessel center was tracked accurately for a range of speeds from 1.4 to 11.2 mm/s.
Keywords :
Kalman filters; biomedical ultrasonics; image segmentation; medical image processing; real-time systems; target tracking; ultrasonic imaging; Star-Kalman algorithm; ellipse parameters; elliptical model; expert segmentation; expert tracings; maximum feature dimension; real time vessel segmentation; real time vessel tracking; segmented contours; sensorized ultrasound probe; simulated ultrasound data; temporal Kalman filter; transverse cross sectional vessel area; transverse vessel area; ultrasound imaging applications; velocity 1.4 mm/s to 11.2 mm/s; vessel center tracking; vessel contour determination; Biomedical imaging; Computational modeling; Image segmentation; Medical diagnostic imaging; Parameter estimation; Probes; Standardization; Ultrasonic imaging; Ultrasonic variables measurement; Veins; Deep venous thrombosis; Kalman filtering; image segmentation; ultrasound; vessel tracking; Algorithms; Artificial Intelligence; Blood Vessels; Computer Systems; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2007.899180
Filename :
4280891
Link To Document :
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