Removal of vesicle structures from transmission electron microscope images

Research output: Contribution to journalJournal articlepeer-review

  • Katrine Hommelhoff Jensen
  • Fred J. Sigworth
  • Sami Sebastian Brandt

In this paper, we address the problem of imaging membrane proteins for single-particle cryo-electron microscopy reconstruction of the isolated protein structure. More precisely, we propose a method for learning and removing the interfering vesicle signals from the micrograph, prior to reconstruction. In our approach, we estimate the subspace of the vesicle structures and project the micrographs onto the orthogonal complement of this subspace. We construct a 2D statistical model of the vesicle structure, based on higher order singular value decomposition (HOSVD), by considering the structural symmetries of the vesicles in the polar coordinate plane. We then propose to lift the HOSVD model to a novel hierarchical model by summarizing the multidimensional HOSVD coefficients by their principal components. Along with the model, a solid vesicle normalization scheme and model selection criterion are proposed to make a compact and general model. The results show that the vesicle structures are accurately separated from the background by the HOSVD model that is also able to adapt to the asymmetries of the vesicles. This is a promising result and suggests even wider applicability of the proposed approach in learning and removal of statistical structures.

Original languageEnglish
JournalIEEE Transactions on Image Processing
Volume25
Issue number2
Pages (from-to)540-552
Number of pages13
ISSN1057-7149
DOIs
Publication statusPublished - 2016

    Research areas

  • Algorithms, Cytoplasmic Vesicles, Image Processing, Computer-Assisted, Membrane Proteins, Microscopy, Electron, Transmission, Models, Biological, Models, Statistical, Signal Processing, Computer-Assisted, Journal Article

ID: 168252184