Collaborative detection of common lines in cryo EM images using maximum likelihood

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a maximum likelihood (ML) algorithm for detecting (shared) common lines between pairs of cryo-EM projection images. The algorithm is based on a global iterative detector in which we jointly estimate and classify common lines using the data from all projection images. We demonstrate by simulations that the algorithm improves the detection rate of common lines compared to state of the art methods, and operates well even with non white imaging noise.

Original languageEnglish
Title of host publication2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479959877
DOIs
StatePublished - 2014
Event2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014 - Eilat, Israel
Duration: 3 Dec 20145 Dec 2014

Publication series

Name2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014

Conference

Conference2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
Country/TerritoryIsrael
CityEilat
Period3/12/145/12/14

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Collaborative detection of common lines in cryo EM images using maximum likelihood'. Together they form a unique fingerprint.

Cite this