Automatic Common Carotid Artery and Internal Jugular Vein Identification and Tracking for the Sonic Flashlight PRESENTED BY

We have built a fully automated ultrasound analysis system that tracks and identifies the common carotid artery (CCA) and the internal jugular vein (IJV). The system is intended to be used in conjunction with the Sonic Flashlight (SF), an augmented reality device that provides real-time in situ visualization of ultrasound images by reflecting calibrated images into a patient's body. Our goal is to prevent inadvertent damage to the CCA when targeting the IJV for catheterization. The automated system starts by identifying and fitting ellipses to all the regions that look like major arteries or veins throughout each B-mode ultrasound image frame. The Spokes Ellipse algorithm described in this thesis tracks these putative vessels and calculates their characteristics, which are then weighted and summed to identify the vessels. The optimum subset of characteristics and their weights were determined from a training set of 38 subjects, whose necks were scanned with a portable 10 MHz ultrasound system at 10 fps. Wilks' Lambda analysis narrowed the characteristics to the five that best distinguish between the CCA and IJV. A paired version of Fisher's linear discriminant analysis was used to calculate the weights for each of the five parameters. Leave-one-out validation studies showed that the system could track and identify the CCA and IJV with 100% accuracy in this data set. The system's limitations and future work are also discussed in this thesis. iii Acknowledgments