Tracking and data association
Robot-Assisted Diagnostic Ultrasound - Design and Feasibility Experiments
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Diffusion-Based Detection of Carotid Artery Lumen from Ultrasound Images
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Dynamic edge tracing: Boundary identification in medical images
Computer Vision and Image Understanding
Carotid artery and jugular vein tracking and differentiation using spatiotemporal analysis
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Carotid artery ultrasound image segmentation using fuzzy region growing
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Detection of arterial lumen in sonographic images based on active contours and diffusion filters
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Fuzzy C-means clustering for segmenting carotid artery ultrasound images
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Carotid ultrasound segmentation using DP active contours
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
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This paper presents the development of a novel, fully automatic tracking and segmentation system to extract the boundary of the carotid artery from ultrasound images in real-time. The center of the carotid artery is tracked by using the Star algorithm. The stability of the Star algorithm has been improved by using a temporal Kalman filter. A spatial Kalman filter is used to estimate the carotid artery boundary. Since the method does not employ any numerical optimization, convergence is very fast. The stability and accuracy of the method is demonstrated by tracking the carotid artery over a 30 second sequence of ultrasound images taken during a carotid artery examination. An application of the tracking method to ultrasound image servoing is also presented.