This talk will present some of the computational and technical image processing challenges we face when processing large amounts of electron tomography data. Electron tomography provides opportunities to determine three-dimensional cellular architecture at resolutions high enough to identify individual macromolecules such as proteins. Image analysis of such data poses a challenging problem due to the extremely low signal-to-noise ratios that makes individual volumes simply too noisy to allow reliable structural interpretation. Complex entities such as cells and viruses, nevertheless, contain multiple copies of numerous macromolecules that can individually be subjected to 3D averaging to boost the signal-to-noise ratios. I will report progress in addressing some of these challenges which involve dealing with the missing wedge of information characteristic of limited angle tomography, the need for robust and computationally efficient 3D image segmentation and alignment routines, and the design of methods that account for diverse conformations through the use of clustering and classification.
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