title: 3D radio frequency ultrasound cardiac segmentation using a linear predictor.
abstract: We present an approach for segmenting the left ventricular endocardial boundaries from radio-frequency (RF) ultrasound. The method employs a computationally efficient two-frame linear predictor which exploits the spatio-temporal coherence of the data. By performing segmentation using the RF data we are able to overcome problems due to image inhomogeneities that are often amplified in B-mode segmentation, as well as provide geometric constraints for RF phase-based speckle tracking. We illustrate the advantages of our approach by comparing it to manual tracings of B-mode data and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 28 3D sequences acquired from 6 canine studies, imaged both at baseline and 1 hour post infarction.
authors.affiliation: Department of Electrical Engineering, Yale University, New Haven, CT, USA. paul.pearlman@yale.edu
authors.forename: Paul C
authors.initials: PC
authors.lastname: Pearlman
authors.forename: Hemant D
authors.initials: HD
authors.lastname: Tagare
authors.forename: Albert J
authors.initials: AJ
authors.lastname: Sinusas
authors.forename: James S
authors.initials: JS
authors.lastname: Duncan
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004285:Dogs; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016014:Linear Models; D008955:Models, Cardiovascular; D009203:Myocardial Infarction / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011846:Radio Waves; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity