title: Towards ultrasound cardiac image segmentation based on the radiofrequency signal.
abstract: In echocardiography, the radio-frequency (RF) image is a rich source of information about the investigated tissues. Nevertheless, very few works are dedicated to boundary detection based on the RF image, as opposed to envelope image. In this paper, we investigate the feasibility and limitations of boundary detection in echocardiographic images based on the RF signal. We introduce two types of RF-derived parameters: spectral autoregressive parameters and velocity-based parameters, and we propose a discontinuity adaptive framework to perform the detection task. In classical echographic cardiac acquisitions, we show that it is possible to use the spectral contents for boundary detection, and that improvement can be expected with respect to traditional methods. Using the system approach, we study on simulations how the spectral contents can be used for boundary detection. We subsequently perform boundary detection in high frame rate simulated and in vivo cardiac sequences using the variance of velocity, obtaining very promising results. Our work opens the perspective of a RF-based framework for ultrasound cardiac image segmentation and tracking.
authors.affiliation: CREATIS, INSA, Bâtiment Blaise Pascal, 69621 Villeurbanne Cedex, France. dydenko@creatis.insa-lyon.fr
authors.forename: Igor
authors.initials: I
authors.lastname: Dydenko
authors.forename: Denis
authors.initials: D
authors.lastname: Friboulet
authors.forename: Jean-Marie
authors.initials: JM
authors.lastname: Gorce
authors.forename: Jan
authors.initials: J
authors.lastname: D'hooge
authors.forename: Bart
authors.initials: B
authors.lastname: Bijnens
authors.forename: Isabelle E
authors.initials: IE
authors.lastname: Magnin
mesh_terms: D000465:Algorithms; D004452:Echocardiography / Q000379:methods*; D005240:Feasibility Studies; D006321:Heart / Q000502:physiology*; D006352:Heart Ventricles / Q000000981:diagnostic imaging; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated; D011846:Radio Waves; D016276:Ventricular Function