- Toward the automatic detection of coronary artery calcification in non-contrast computed tomography dataGerd Brunner
Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, TX, USA
Int J Cardiovasc Imaging 26:829-38. 2010..Further, the CAR classifiers are able to detect CACs with a mean sensitivity and specificity of 86.33 and 93.78%, respectively...
- Automatic quantification of muscle volumes in magnetic resonance imaging scans of the lower extremitiesGerd Brunner
Division of Atherosclerosis and Vascular Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
Magn Reson Imaging 29:1065-75. 2011..Experiments for the fully automatic detection of muscle volume in MRI scans demonstrated an averaged accuracy, sensitivity and specificity for leave-one-out cross-validation of 88.3%, 93.6% and 87.2%, respectively...
- A supervised classification-based method for coronary calcium detection in non-contrast CTUday Kurkure
Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, TX 77204, USA
Int J Cardiovasc Imaging 26:817-28. 2010..Our method detected coronary calcifications with an accuracy, sensitivity and specificity of 98.27, 92.07 and 98.62%, respectively, for a testing dataset of non-contrast computed tomography scans from 105 subjects...
- Segmental analysis of carotid arterial strain using speckle-trackingEric Y Yang
Department of Medicine, Baylor College of Medicine, Houston, Texas 77030, USA
J Am Soc Echocardiogr 24:1276-1284.e5. 2011..The aims of this study were to evaluate whether speckle-tracking could be used to measure carotid arterial strain (CAS) reproducibly in healthy volunteers and to determine if CAS was lesser in individuals with diabetes...