title: LV segmentation through the analysis of radio frequency ultrasonic images.
abstract: LV segmentation is often an important part of many automated cardiac diagnosis strategies. However, the segmentation of echocardiograms is a difficult task because of poor image quality. In echocardiography, we note that radio-frequency (RF) signal is a rich source of information about the moving LV as well. In this paper, first, we will investigate currently used, important RF derived parameters: integrated backscatter coefficient (IBS), mean central frequency (MCF) and the maximum correlation coefficients (MCC) from speckle tracking. Second, we will develop a new segmentation algorithm for the segmentation of the LV boundary, which can avoid local minima and leaking through uncompleted boundary. Segmentations are carried out on the RF signal acquired from a Sonos7500 ultrasound system. The results are validated by comparing to manual segmentation results.
authors.affiliation: Yale University, University of Michigan, Philips Research, University of Washington, USA.
authors.forename: P
authors.initials: P
authors.lastname: Yan
authors.forename: C X
authors.initials: CX
authors.lastname: Jia
authors.forename: A
authors.initials: A
authors.lastname: Sinusas
authors.forename: K
authors.initials: K
authors.lastname: Thiele
authors.forename: M
authors.initials: M
authors.lastname: O'Donnell
authors.forename: J S
authors.initials: JS
authors.lastname: Duncan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004452:Echocardiography / Q000379:methods*; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011846:Radio Waves; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity