Segmentation of arterial geometry from ultrasound images using balloon models

Chen, C.; Poepping, Tamie L.; Beech-Brandt, J.J.; Hammer, Steven; Baldock, Richard; Hill, Bill; Allan, Paul L.; Easson, W. J.; Hoskins, Peter

doi:10.1109/isbi.2004.1398789

Cited by 5 publications

References 5 publications

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Automatic identification of blood vessel cross-section for central venous catheter placement using a cascading classifier

Ikhsan

Tan

Putra

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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This paper presents a system that is able to automatically identify, segment, and track the cross-section of the internal jugular vein (IJV) and the common carotid artery (CCA) in an ultrasound image feed during a central venous catheter (CVC) placement procedure. The goal is to provide assistance to the practitioner in order to decrease the probability of complications stemming from inadvertent punctures of the CCA during the procedure. In the system, a modified Star algorithm is implemented to segment and track the blood vessel throughout an ultrasound video feed. A novel algorithm based on a cascading classifier is used to identify the location of the IJV and the CCA for two main tasks: (1) selecting the initial seed point at the start of tracking and (2) validating the segmentation results at each subsequent frame. The classifier uses shape features (vessel area, ellipse fitting error, vessel depth, vessel eccentricity) and pixel-based features (pixel intensity and the histogram of oriented gradients descriptor) to differentiate between vessel and non-vessel structures and also differentiate between the IJV and the CCA. Evaluated on a database of 800 ultrasound images containing the cross-section of both vessels, the cascading classifier was able to identify the IJV and the CCA in 92.25% and 85.13% of the images respectively without any initialization from the user at a maximum processing rate of 40.65 frames per second. This allows identification to be conducted in real-time with existing ultrasound machines.

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Automatic identification of blood vessel cross-section for central venous catheter placement using a cascading classifier

Ikhsan

Tan

Putra

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Novel Multistage Three-Dimensional Medical Image Segmentation: Methodology and Validation

Peters

2006

IEEE Trans. Inform. Technol. Biomed.

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In this paper, we propose a novel multistage method for three-dimensional (3-D) segmentation of medical images and a new radial distance-based segmentation validation approach. For the 3-D segmentation method, we first employ a morphological recursive erosion operation to reduce the connectivity between the region of interest and its surrounding neighborhood; then we design a hybrid segmentation method to achieve an initial result. The hybrid approach integrates an improved fast marching method and a morphological reconstruction algorithm. Finally, a morphological recursive dilation is employed to recover any lost structure from the first stage of the multistage method. This approach is tested on 12 CT and 3 MRI images of the brain, heart, and kidney, to demonstrate the effectiveness and accuracy of this technique across a variety of imaging modalities and organ systems. In order to validate the multistage segmentation method, a novel radial distance-based validation method is proposed that uses a global accuracy (GA) measure. The GA is calculated based on local radial distance errors (LRDE), where LRDE are calculated on the radii emitted from points along the skeleton of the object rather than the centroid, in order to accommodate more complicated organ structures. The experimental results demonstrate that the proposed multistage segmentation method is fast and accurate, with comparable performance to existing segmentation methods, but with a significantly higher execution speed.

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Real-Time Vessel Segmentation and Tracking for Ultrasound Imaging Applications

Guerrero

Salcudean

McEwen

et al. 2007

IEEE Trans. Med. Imaging

121

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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.

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Segmentation of arterial geometry from ultrasound images using balloon models

Cited by 5 publications

References 5 publications

Automatic identification of blood vessel cross-section for central venous catheter placement using a cascading classifier

Automatic identification of blood vessel cross-section for central venous catheter placement using a cascading classifier

Novel Multistage Three-Dimensional Medical Image Segmentation: Methodology and Validation

Real-Time Vessel Segmentation and Tracking for Ultrasound Imaging Applications

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