Gerard van Burken

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A fully automated segmentation for 3D echocardiography (3DE) using 3D Active Appearance Models (AAM) was developed and evaluated on 99 patients. The method used ultrasound specific grey value normalization and two matching algorithms were tested. To our knowledge this is the first report on a fully operational 3D AAM employed in 3DE on a large scale. The 3D(More)
Three-dimensional (3-D) stress echocardiography is a novel technique for diagnosing cardiac dysfunction. It involves evaluating wall motion of the left ventricle, by visually analyzing ultrasound images obtained in rest and in different stages of stress. Since the acquisitions are performed minutes apart, variabilities may exist in the visualized(More)
Intraplaque neovascularization (IPN) is an important biomarker of atherosclerotic plaque vulnerability. As IPN can be detected by contrast enhanced ultrasound (CEUS), imaging-biomarkers derived from CEUS may allow early prediction of plaque vulnerability. To select the best quantitative imaging-biomarkers for prediction of plaque vulnerability, a systematic(More)
We propose a semi-automatic endocardial border detection method for 3D+T cardiac ultrasound data based on pattern matching and dynamic programming, operating on 2D slices of the 3D+T data, for the estimation of LV volume, with minimal user interaction. It shows good correlations with MRI ED and ES volumes (r=0.938) and low interobserver variability(More)
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