A method for the alignment of heterogeneous macromolecules from electron microscopy. Export

@article{citeulike:5819481, abstract = {We propose a feature-based image alignment method for single-particle electron microscopy that is able to accommodate various similarity scoring functions while efficiently sampling the two-dimensional transformational space. We use this image alignment method to evaluate the performance of a scoring function that is based on the Mutual Information (MI) of two images rather than one that is based on the cross-correlation function. We show that alignment using MI for the scoring function has far less model-dependent bias than is found with cross-correlation based alignment. We also demonstrate that MI improves the alignment of some types of heterogeneous data, provided that the signal-to-noise ratio is relatively high. These results indicate, therefore, that use of MI as the scoring function is well suited for the alignment of class-averages computed from single-particle images. Our method is tested on data from three model structures and one real dataset.}, author = {Shatsky, Maxim and Hall, Richard J. and Brenner, Steven E. and Glaeser, Robert M.}, citeulike-article-id = {5819481}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.jsb.2008.12.008}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19166941}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19166941}, doi = {10.1016/j.jsb.2008.12.008}, issn = {1095-8657}, journal = {Journal of structural biology}, keywords = {heterogeneity, image\_processing, structure}, month = {April}, number = {1}, pages = {67--78}, posted-at = {2009-09-22 10:05:34}, priority = {2}, title = {A method for the alignment of heterogeneous macromolecules from electron microscopy.}, url = {http://dx.doi.org/10.1016/j.jsb.2008.12.008}, volume = {166}, year = {2009} }

TY - JOUR ID - citeulike:5819481 L3 - citeulike-article-id:5819481 N2 - We propose a feature-based image alignment method for single-particle electron microscopy that is able to accommodate various similarity scoring functions while efficiently sampling the two-dimensional transformational space. We use this image alignment method to evaluate the performance of a scoring function that is based on the Mutual Information (MI) of two images rather than one that is based on the cross-correlation function. We show that alignment using MI for the scoring function has far less model-dependent bias than is found with cross-correlation based alignment. We also demonstrate that MI improves the alignment of some types of heterogeneous data, provided that the signal-to-noise ratio is relatively high. These results indicate, therefore, that use of MI as the scoring function is well suited for the alignment of class-averages computed from single-particle images. Our method is tested on data from three model structures and one real dataset. IS - 1 JF - Journal of structural biology SN - 1095-8657 EP - 78 TI - A method for the alignment of heterogeneous macromolecules from electron microscopy. VL - 166 SP - 67 KW - heterogeneity KW - image_processing KW - structure AU - Shatsky, Maxim AU - Hall, Richard AU - Brenner, Steven AU - Glaeser, Robert PY - 2009/04// UR - http://dx.doi.org/10.1016/j.jsb.2008.12.008 ER -