@article {2113, title = {Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification.}, journal = {J Struct Biol}, volume = {164}, year = {2008}, month = {10/2008}, pages = {41-8}, abstract = {

As collection of electron microscopy data for single-particle reconstruction becomes more efficient, due to electronic image capture, one of the principal limiting steps in a reconstruction remains particle-verification, which is especially costly in terms of user input. Recently, some algorithms have been developed to window particles automatically, but the resulting particle sets typically need to be verified manually. Here we describe a procedure to speed up verification of windowed particles using multivariate data analysis and classification. In this procedure, the particle set is subjected to multi-reference alignment before the verification. The aligned particles are first binned according to orientation and are binned further by K-means classification. Rather than selection of particles individually, an entire class of particles can be selected, with an option to remove outliers. Since particles in the same class present the same view, distinction between good and bad images becomes more straightforward. We have also developed a graphical interface, written in Python/Tkinter, to facilitate this implementation of particle-verification. For the demonstration of the particle-verification scheme presented here, electron micrographs of ribosomes are used.

}, keywords = {Algorithms, Artificial Intelligence, Classification, Image Enhancement, Image Processing, Computer-Assisted, Microscopy, Electron, Multivariate Analysis, Ribosomes}, issn = {1095-8657}, doi = {10.1016/j.jsb.2008.06.006}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18619547}, author = {Shaikh, Tanvir R and Trujillo, Ramon and LeBarron, Jamie and Baxter, Bill and Frank, Joachim} } @article {2115, title = {Particle picking by segmentation: a comparative study with SPIDER-based manual particle picking.}, journal = {J Struct Biol}, volume = {152}, year = {2005}, month = {12/2005}, pages = {211-20}, abstract = {

Boxing hundreds of thousands of particles in low-dose electron micrographs is one of the major bottle-necks in advancing toward achieving atomic resolution reconstructions of biological macromolecules. We have shown that a combination of pre-processing operations and segmentation can be used as an effective, automatic tool for identifying and boxing single-particle images. This paper provides a brief description of how this method has been applied to a large data set of micrographs of ice-embedded ribosomes, including a comparative analysis of the efficiency of the method. Some results on processing micrographs of tripeptidyl peptidase II particles are also shown. In both cases, we have achieved our goal of selecting at least 80\% of the particles that an expert would select with less than 10\% false positives.

}, keywords = {Algorithms, Aminopeptidases, Cryoelectron Microscopy, Dipeptidyl-Peptidases and Tripeptidyl-Peptidases, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Internet, Particle Size, Ribosomes, Serine Endopeptidases, Software, Software Validation}, issn = {1047-8477}, doi = {10.1016/j.jsb.2005.09.007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16330229}, author = {Adiga, Umesh and Baxter, Bill and Hall, Richard J and Rockel, Beate and Rath, Bimal K and Frank, Joachim and Glaeser, Robert M} }