<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adiga, Umesh</style></author><author><style face="normal" font="default" size="100%">Baxter, Bill</style></author><author><style face="normal" font="default" size="100%">Hall, Richard J</style></author><author><style face="normal" font="default" size="100%">Rockel, Beate</style></author><author><style face="normal" font="default" size="100%">Rath, Bimal K</style></author><author><style face="normal" font="default" size="100%">Frank, Joachim</style></author><author><style face="normal" font="default" size="100%">Glaeser, Robert M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Particle picking by segmentation: a comparative study with SPIDER-based manual particle picking.</style></title><secondary-title><style face="normal" font="default" size="100%">J Struct Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Struct. Biol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Aminopeptidases</style></keyword><keyword><style  face="normal" font="default" size="100%">Cryoelectron Microscopy</style></keyword><keyword><style  face="normal" font="default" size="100%">Dipeptidyl-Peptidases and Tripeptidyl-Peptidases</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Imaging, Three-Dimensional</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Particle Size</style></keyword><keyword><style  face="normal" font="default" size="100%">Ribosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Serine Endopeptidases</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Software Validation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2005</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/16330229</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">152</style></volume><pages><style face="normal" font="default" size="100%">211-20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;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.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adiga, Umesh</style></author><author><style face="normal" font="default" size="100%">Malladi, Ravi</style></author><author><style face="normal" font="default" size="100%">Baxter, Bill</style></author><author><style face="normal" font="default" size="100%">Glaeser, Robert M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A binary segmentation approach for boxing ribosome particles in cryo EM micrographs.</style></title><secondary-title><style face="normal" font="default" size="100%">J Struct Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Struct. Biol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Anisotropy</style></keyword><keyword><style  face="normal" font="default" size="100%">Automatic Data Processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Cryoelectron Microscopy</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Enhancement</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Particle Size</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Automated</style></keyword><keyword><style  face="normal" font="default" size="100%">Ribosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Software Design</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2004</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/15065681</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">145</style></volume><pages><style face="normal" font="default" size="100%">142-51</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Three-dimensional reconstruction of ribosome particles from electron micrographs requires selection of many single-particle images. Roughly 100,000 particles are required to achieve approximately 10 A resolution. Manual selection of particles, by visual observation of the micrographs on a&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;computer&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;screen, is recognized as a bottleneck in automated single-particle reconstruction. This paper describes an efficient approach for automated boxing of ribosome particles in micrographs. Use of a fast, anisotropic non-linear reaction-diffusion method to pre-process micrographs and rank-leveling to enhance the contrast between particles and the background, followed by binary and morphological segmentation constitute the core of this technique. Modifying the shape of the particles to facilitate segmentation of individual particles within clusters and boxing the isolated particles is successfully attempted. Tests on a limited number of micrographs have shown that over 80% success is achieved in automatic particle picking.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1-2</style></issue></record></records></xml>