Intensity-Based Skeletonization of CryoEM Gray-Scale Images Using a True Segmentation-Free Algorithm
Issue No. 05 - Sept.-Oct. (2013 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.121
Kamal Al Nasr , Dept. of Comput. Sci., Tennessee State Univ., Nashville, TN, USA
Chunmei Liu , Dept. of Syst. & Comput. Sci., Howard Univ., Washington, DC, USA
Mugizi Rwebangira , Dept. of Syst. & Comput. Sci., Howard Univ., Washington, DC, USA
Legand Burge , Dept. of Syst. & Comput. Sci., Howard Univ., Washington, DC, USA
Jing He , Dept. of Comput. Sci., Old Dominion Univ., Norfolk, VA, USA
Cryo-electron microscopy is an experimental technique that is able to produce 3D gray-scale 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 cannot 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 3D gray-scale 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 gray-scale curve-like skeletons. The approach relies on a novel representation of the 3D 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 percent for Gorgon and DP-TOSS, respectively.
Skeleton, Proteins, Gray-scale, Algorithm design and analysis, Solid modeling, Microscopy
K. Al Nasr, Chunmei Liu, M. Rwebangira, L. Burge and Jing He, "Intensity-Based Skeletonization of CryoEM Gray-Scale Images Using a True Segmentation-Free Algorithm," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 5, pp. 1289-1298, 2014.