Myocardial border detection by branch-and-bound dynamic programming in magnetic resonance images

Dynamic programming (DP) is a mathematical technique for making optimal decisions on the sequencing of interrelated problems. It has been used widely to detect borders in magnetic resonance images (MRI). MRI is noninvasive and generates clear images; however, it is impractical for manual measurement of the huge number of images generated by dynamic organs such as those of the cardiovascular system. A fast and effective algorithm is essential for on-line implementation of MRI-based computer aided measurement and diagnosis. In this paper, a branch-and-bound dynamic programming technique is applied to detect the endocardial borders of the left ventricular. The proposed branch-and-bound method drastically reduces the computational time required in conventional exhaustive search methods. Statistical tests are conducted to verify the CPU time performance of the branch-and-bound technique in comparison to the conventional exhaustive search method.

[1]  J C Fu,et al.  De-noising of left ventricular myocardial borders in magnetic resonance images. , 2002, Magnetic resonance imaging.

[2]  Gregory A. Baxes,et al.  Digital image processing - principles and applications , 1994 .

[3]  G. Hamarneh,et al.  Combining snakes and active shape models for segmenting the human left ventricle in echocardiographic images , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[4]  Haiyan Wang,et al.  Geometric active deformable models in shape modeling , 2000, IEEE Trans. Image Process..

[5]  P. K. Dutta,et al.  A GA based approach for boundary detection of left ventricle with echocardiographic image sequences , 2003, Image Vis. Comput..

[6]  T W Redpath,et al.  Determination of normal regional left ventricular function from cine-MR images using a semi-automated edge detection method. , 1999, Magnetic resonance imaging.

[7]  Johan H. C. Reiber,et al.  A semi-automatic endocardial border detection method for the left ventricle in 4D ultrasound data sets , 2004, CARS.

[8]  D. Adam,et al.  Automatic ventricular cavity boundary detection from sequential ultrasound images using simulated annealing. , 1989, IEEE transactions on medical imaging.

[9]  Steven R. Fleagle,et al.  Methods of graph searching for border detection in image sequences with applications to cardiac magnetic resonance imaging , 1995, IEEE Trans. Medical Imaging.

[10]  Andrzej Materka,et al.  Two-phase active contour method for semiautomatic segmentation of the heart and blood vessels from MRI images for 3D visualization. , 2002, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[11]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[12]  Frederick S. Hillier,et al.  Introduction of Operations Research , 1967 .

[13]  Robert M. Haralick,et al.  A knowledge-based boundary delineation system for contrast ventriculograms , 2001, IEEE Transactions on Information Technology in Biomedicine.

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

[15]  Geir Storvik,et al.  A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

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

[17]  Russell M. Mersereau,et al.  Knowledge-based system for boundary detection of four-dimensional cardiac magnetic resonance image sequences , 1993, IEEE Trans. Medical Imaging.

[18]  J. C. Fu,et al.  Wavelet-based enhancement for detection of left ventricular myocardial boundaries in magnetic resonance images. , 2000, Magnetic resonance imaging.

[19]  H Bunke,et al.  Left ventricular boundary detection from spatio-temporal volumetric computed tomography images. , 1995, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[20]  E J Delp,et al.  Detecting left ventricular endocardial and epicardial boundaries by digital two-dimensional echocardiography. , 1988, IEEE transactions on medical imaging.

[21]  N Reichek,et al.  Multicenter trial of automated border detection in cardiac MR imaging , 1993, Journal of magnetic resonance imaging : JMRI.

[22]  Surendra Ranganath,et al.  Contour extraction from cardiac MRI studies using snakes , 1995, IEEE Trans. Medical Imaging.

[23]  J. Reiber,et al.  Comparison between manual and semiautomated analysis of left ventricular volume parameters from short-axis MR images. , 1997, Journal of computer assisted tomography.

[24]  James S. Duncan,et al.  Boundary Finding with Parametrically Deformable Models , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  J. Ehrhardt,et al.  Automated identification of left ventricular borders from spin-echo magnetic resonance images. Experimental and clinical feasibility studies. , 1991, Investigative radiology.