Chandrajit Bajaj
 Introduction | Visualization Tool | Filtering | Gradient Vector Diffusion | Segmentation | Skeletonization | References

1. Introduction

Most if not all proteins in a cell are organized into cellular machines that are built from up to several dozens of individual proteins. For such multi-protein complexes, electron tomographic imaging provides the only foreseeable way to obtain 3D structural information. All other structural techniques such as spectroscopic, diffraction or single-particle analysis cryo-electron microscopic techniques rely implicitly or explicitly on averaging of a large number of identical particles. Electron tomography, in contrast, can provide 3D structural information of such unique volumes as whole cells. Although cellular tomographic imaging is no means a new technique, only recently it has received more attention. While recording devices (CCDs) are becoming larger, and data collection becomes faster, the bottleneck in this emerging field lies more and more on the visualization and interpretation of the tomograms. So why are tomograms so much harder to study and interpret? The answer may lie in the following co-mingled reasons: First, most tomograms exhibit a very low signal-to-noise ratio. Second, the cellular machine does not reside in isolation but are embedded in their cellular context, and densely surrounded by other proteins that may or may not directly interact with the cellular machine. Third, we don't know the exact composition and conformation of cellular machines at the time of investigation.

The poor signal-to-noise ratio usually observed in tomograms complicates the visualization of the volume as well as the automated feature extraction. Hence, noise reduction is always in demand as a pre-processing step to improve the signal-to-noise ratio. Segmentation is often necessary to obtain an unobstructed view into the machinery's architectural organization, and to reduce the complexity of the scenery to allow for biological interpretation. Feature extraction is particularly challenging if cellular machine of interest is in close contact to its cellular surrounding, and if there is no preconception of its 3D structure. In such cases, manual segmentation approaches appear somewhat subjective and become less feasible even with the help of 3D data re-slicing along non-orthogonal angles to obtain a more favorable view, and sophisticated graphics tools. Moreover, they are unlikely to keep up with the amount of data that can be generated by modern-day electron microscope data collection schemes. The complexity of cellular 3D volumes requires some form of data reduction and simplification. Skeletonization may be a way to simplify 3D data sets while retaining their characteristics, which is also important in comparing two complexes that are similar but not identical. Skeletons will be helpful in comparing two such cellular machines and describing their similarities and discrepancies.