Space-time Signature Analysis of 2D Echocardiograms Based on Topographic Cellular Active Contour Techniques

A novel experimental system is introduced with the ultimate aim of providing a semi-automated tool for clinicians to quantify cardiac function of the left ventricle (LV) using two-chamber transthoracic 2D video flows. The algorithm extracts space-time signatures from echocardiograms based on real-time pixel-level parallel boundary tracking methods (topographic cellular active contour techniques) that are used to drive learning and recognition modules in order to enhance reliability of diagnoses made up by clinicians. In extraction process subsequent ellipse model based wall-segment identification, sampling and filtering are used, furthermore specific wall segment thickening models for region of interest (ROI) selection and reliability enhancement of segment identification and signature interpretation. Simple learning algorithms like decision tree and fuzzy produced adequate results in the first stage of experiments on large database of patient records

[1]  M.S. Pattichis,et al.  M-mode echocardiography image and video segmentation based on am-fm demodulation techniques , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[2]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[3]  Tamás Roska,et al.  CNN-based spatio-temporal nonlinear filtering and endocardial boundary detection in echocardiography , 1999 .

[4]  José M. N. Leitão,et al.  Wall position and thickness estimation from sequences of echocardiographic images , 1996, IEEE Trans. Medical Imaging.

[5]  J. Alison Noble,et al.  A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography , 2002, IEEE Transactions on Medical Imaging.

[6]  E. Caiani,et al.  Quantitative analysis of myocardial perfusion and regional left ventricular function from contrast-enhanced power modulation images , 2001, Computers in Cardiology 2001. Vol.28 (Cat. No.01CH37287).

[7]  B. Bijnens,et al.  An open environment for quantification of left ventricular function using ultrasound images , 1993, Proceedings of Computers in Cardiology Conference.

[8]  Johan Montagnat,et al.  Cylindrical Echocardiographic Image Segmentation Based on 3D Deformable Models , 1999, MICCAI.

[9]  Csaba Rekeczky,et al.  Topographic cellular active contour techniques: theory, implementations and comparisons , 2006, Int. J. Circuit Theory Appl..

[10]  Yongmin Kim,et al.  A multiple active contour model for cardiac boundary detection on echocardiographic sequences , 1996, IEEE Trans. Medical Imaging.

[11]  Tamás Roska,et al.  CNN‐based spatio‐temporal nonlinear filtering and endocardial boundary detection in echocardiography , 1999, Int. J. Circuit Theory Appl..

[12]  Leon O. Chua,et al.  Computing with Front Propagation: Active Contour And Skeleton Models In Continuous-Time CNN , 1999, J. VLSI Signal Process..