Intensity-Based Skeletonization of CryoEM Gray-Scale Images Using a True Segmentation-Free Algorithm

Kamal Al Nasr, Chunmei Liu, Mugizi Rwebangira, Legand Burge, Jing He

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number6731359
Pages (from-to)1289-1298
Number of pages10
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume10
Issue number5
DOIs
StatePublished - Sep 2013
Externally publishedYes

Keywords

  • graphs
  • Image processing
  • modeling techniques
  • volumetric image representation

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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