Markovian Level Set: A New Method FOr Boundary Detection From Echocardiographic Images

Computer-based boundary detection in echocardiographic images is a challenging problem, due to the large amount of noise and the poor contrast presented. In this paper, a Markovian level set method is proposed for boundary detection in long-axis echocardiographic images. It combines MRP model which makes use of local statistics with level set method which handles topological changes, to ensure that the resulting boundary is continuous and smooth. Experimental results show that high accuracy is achieved with the proposed method

[1]  Yongmin Kim,et al.  A methodology for evaluation of boundary detection algorithms on medical images , 1997, IEEE Transactions on Medical Imaging.

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

[3]  Gerhard Winkler,et al.  Image analysis, random fields and dynamic Monte Carlo methods: a mathematical introduction , 1995, Applications of mathematics.

[4]  Donald A. Jackson,et al.  Similarity Coefficients: Measures of Co-Occurrence and Association or Simply Measures of Occurrence? , 1989, The American Naturalist.

[5]  Alessandro Sarti,et al.  Left ventricular volume estimation for real-time three-dimensional echocardiography , 2002, IEEE Transactions on Medical Imaging.

[6]  James S. Duncan,et al.  Combinative Multi-scale Level Set Framework for Echocardiographic Image Segmentation , 2002, MICCAI.

[7]  Kevin J. Parker,et al.  Multiple Resolution Bayesian Segmentation of Ultrasound Images , 1994, Other Conferences.

[8]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[9]  Olivier Basset,et al.  Segmentation of ultrasound images--multiresolution 2D and 3D algorithm based on global and local statistics , 2003, Pattern Recognit. Lett..

[10]  N. Paragios.  A level set approach for shape-driven segmentation and tracking of the left ventricle , 2003, IEEE Transactions on Medical Imaging.