DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.121
Kamal Al Nasr , Howard University, Washington D.C.
Chunmei Liu , Howard University, Washington D.C.
Mugizi Rwebangira , Howard University, Washington D.C.
Legand Burge , Howard University, Washington D.C.
Jing He , Old Dominion University, Norfolk
Cryo-electron microscopy is an experimental technique that is able to produce 3-D grayscale images of protein molecules. In contrast to other experimental techniques, cryo-electron microscopy is capable of visualizing large molecular complexes such as viruses and ribosomes. At medium resolution, the positions of the atoms are not visible and the process can't proceed. The medium-resolution images produced by cryo-electron microscopy are used to derive the atomic structure of the proteins in de novo modeling. The skeletons of the 3-D grayscale images are used to interpret important information that is helpful in de novo modeling. Unfortunately, not all features of the image can be captured using a single segmentation. In this paper, we present a segmentation-free approach to extract the grayscale curve-like skeletons. The approach relies on a novel representation of the 3-D image, where the image is modeled as a graph and a set of volume trees. A test containing 36 synthesized maps and one authentic map shows that our approach can improve the performance of the two tested tools used in de novo modeling. The improvements were 62% and 13% for Gorgon and MapEM, respectively.
Image Processing and Computer Vision, Volumetric
Kamal Al Nasr, Chunmei Liu, Mugizi Rwebangira, Legand Burge, Jing He, "Intensity-Based Skeletonization of CryoEM Grayscale Images Using a True Segmentation-Free Algorithm", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. , no. , pp. 0, 5555, doi:10.1109/TCBB.2013.121