Combining snakes and active shape models for segmenting the human left ventricle in echocardiographic images

The authors propose a method for segmenting the human left ventricle (LV) in ultrasonic images, which is based on principles from both Active Shape Models (ASM) and Active Contour Models (ACM). Principal Component Analysis (PCA) is applied to a frequency-based shape representation of the LV thus eliminating the need for the difficult determination of corresponding landmarks. The average ventricular shape and the set of most significant shape variation modes are obtained from a training set. The LV boundaries in new images are found by placing an initial ACM (Snake) and allowing it to deform only according to the examined shape variations. A training set of 105 expert-segmented echocardiographic images was used. Improvements in the segmentation results were obtained especially in cases where the ventricular boundary was partly occluded by noise.

[1]  Ramesh C. Jain,et al.  Using Dynamic Programming for Solving Variational Problems in Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[3]  John J. Soraghan,et al.  Detection of echocardiographic left ventricle boundaries using neural networks , 1993, Proceedings of Computers in Cardiology Conference.

[4]  Timothy F. Cootes,et al.  Application of point distribution models to the automated analysis of echocardiograms , 1994, Computers in Cardiology 1994.

[5]  A. Herment,et al.  Segmentation of cardiac and vascular ultrasound images with extension to border kinetics , 1994, 1994 Proceedings of IEEE Ultrasonics Symposium.

[6]  M. G. Strintzis,et al.  Bayesian contour estimation of the left ventricle in ultrasound images of the heart , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[7]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[8]  David N. Levin,et al.  "Brownian Strings": Segmenting Images with Stochastically Deformable Contours , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  James D. Thomas,et al.  Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates , 1998, IEEE Transactions on Medical Imaging.

[10]  Michael G. Strintzis,et al.  Tracking the left ventricle in echocardiographic images by learning heart dynamics , 1999, IEEE Transactions on Medical Imaging.

[11]  Demetri Terzopoulos,et al.  T-snakes: Topology adaptive snakes , 2000, Medical Image Anal..