Symmetric inverse consistent nonlinear registration driven by mutual information. Export

@article{citeulike:5495434, abstract = {A nonlinear viscoelastic image registration algorithm based on the demons paradigm and incorporating inverse consistent constraint (ICC) is implemented. An inverse consistent and symmetric cost function using mutual information (MI) as a similarity measure is employed. The cost function also includes regularization of transformation and inverse consistent error (ICE). The uncertainties in balancing various terms in the cost function are avoided by alternatively minimizing the similarity measure, the regularization of the transformation, and the ICE terms. The diffeomorphism of registration for preventing folding and/or tearing in the deformation is achieved by the composition scheme. The quality of image registration is first demonstrated by constructing brain atlas from 20 adult brains (age range 30-60). It is shown that with this registration technique: (1) the Jacobian determinant is positive for all voxels and (2) the average ICE is around 0.004 voxels with a maximum value below 0.1 voxels. Further, the deformation-based segmentation on Internet Brain Segmentation Repository, a publicly available dataset, has yielded high Dice similarity index (DSI) of 94.7\% for the cerebellum and 74.7\% for the hippocampus, attesting to the quality of our registration method.}, author = {Tao, Guozhi and He, Renjie and Datta, Sushmita and Narayana, Ponnada A.}, citeulike-article-id = {5495434}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.cmpb.2009.01.011}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0169260709000443}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/19268386}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=19268386}, doi = {10.1016/j.cmpb.2009.01.011}, issn = {1872-7565}, journal = {Computer methods and programs in biomedicine}, keywords = {icc, mutual-information, registration, symmetric}, month = {August}, number = {2}, pages = {105--115}, posted-at = {2009-10-01 10:02:06}, priority = {2}, title = {Symmetric inverse consistent nonlinear registration driven by mutual information.}, url = {http://dx.doi.org/10.1016/j.cmpb.2009.01.011}, volume = {95}, year = {2009} }

TY - JOUR ID - citeulike:5495434 L3 - citeulike-article-id:5495434 N2 - A nonlinear viscoelastic image registration algorithm based on the demons paradigm and incorporating inverse consistent constraint (ICC) is implemented. An inverse consistent and symmetric cost function using mutual information (MI) as a similarity measure is employed. The cost function also includes regularization of transformation and inverse consistent error (ICE). The uncertainties in balancing various terms in the cost function are avoided by alternatively minimizing the similarity measure, the regularization of the transformation, and the ICE terms. The diffeomorphism of registration for preventing folding and/or tearing in the deformation is achieved by the composition scheme. The quality of image registration is first demonstrated by constructing brain atlas from 20 adult brains (age range 30-60). It is shown that with this registration technique: (1) the Jacobian determinant is positive for all voxels and (2) the average ICE is around 0.004 voxels with a maximum value below 0.1 voxels. Further, the deformation-based segmentation on Internet Brain Segmentation Repository, a publicly available dataset, has yielded high Dice similarity index (DSI) of 94.7% for the cerebellum and 74.7% for the hippocampus, attesting to the quality of our registration method. IS - 2 JF - Computer methods and programs in biomedicine SN - 1872-7565 EP - 115 TI - Symmetric inverse consistent nonlinear registration driven by mutual information. VL - 95 SP - 105 KW - icc KW - mutual-information KW - registration KW - symmetric AU - Tao, Guozhi AU - He, Renjie AU - Datta, Sushmita AU - Narayana, Ponnada PY - 2009/08// UR - http://dx.doi.org/10.1016/j.cmpb.2009.01.011 ER -