Automatic ventricular cavity boundary detection from sequential ultrasound images using simulated annealing Export

@article{Friedland89, abstract = {An automatic algorithm has been developed for high-speed detection of cavity boundaries in sequential 2-D echocardiograms using an optimization algorithm called simulated annealing (SA). The algorithm has three stages. (1) A predetermined window of size <e1>n</e1>\&times; <e1>m</e1> is decimated to size <e1>n</e1>'\&times;<e1>m</e1>' after low-pass filtering. (2) An iterative radial gradient algorithm is employed to determine the center of gravity (CG) of the cavity. (3) 64 radii which originate from the CG defined in stage 2 are bounded by the high-probability region. Each bounded radius is defined as a link in a 1-D, 64-member cyclic Markov random field. This algorithm is unique in that it compounds spatial and temporal information along with a physical model in its decision rule, whereas most other algorithms base their decisions on spatial data alone. This is the first implementation of a relaxation algorithm for edge detection in echocardiograms. Results attained using this algorithm on real data have been highly encouraging}, author = {Friedland, N. and Adam, D.}, booktitle = {Medical Imaging, IEEE Transactions on}, citeulike-article-id = {5658277}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/42.41487}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=41487}, doi = {10.1109/42.41487}, journal = {Medical Imaging, IEEE Transactions on}, keywords = {contour, heart, segmentation, tracking, ultrasound}, number = {4}, pages = {344--353}, posted-at = {2009-08-27 18:05:30}, priority = {2}, title = {Automatic ventricular cavity boundary detection from sequential ultrasound images using simulated annealing}, url = {http://dx.doi.org/10.1109/42.41487}, volume = {8}, year = {1989} }

TY - JOUR ID - Friedland89 L3 - citeulike-article-id:5658277 N2 - An automatic algorithm has been developed for high-speed detection of cavity boundaries in sequential 2-D echocardiograms using an optimization algorithm called simulated annealing (SA). The algorithm has three stages. (1) A predetermined window of size <e1>n</e1>&times; <e1>m</e1> is decimated to size <e1>n</e1>'&times;<e1>m</e1>' after low-pass filtering. (2) An iterative radial gradient algorithm is employed to determine the center of gravity (CG) of the cavity. (3) 64 radii which originate from the CG defined in stage 2 are bounded by the high-probability region. Each bounded radius is defined as a link in a 1-D, 64-member cyclic Markov random field. This algorithm is unique in that it compounds spatial and temporal information along with a physical model in its decision rule, whereas most other algorithms base their decisions on spatial data alone. This is the first implementation of a relaxation algorithm for edge detection in echocardiograms. Results attained using this algorithm on real data have been highly encouraging IS - 4 JF - Medical Imaging, IEEE Transactions on EP - 353 TI - Automatic ventricular cavity boundary detection from sequential ultrasound images using simulated annealing VL - 8 T2 - Medical Imaging, IEEE Transactions on SP - 344 KW - contour KW - heart KW - segmentation KW - tracking KW - ultrasound AU - Friedland, N AU - Adam, D PY - 1989/// UR - http://dx.doi.org/10.1109/42.41487 ER -