Image segmentation for automatic particle identification in electron micrographs based on hidden Markov random field models and expectation maximization
Abbreviated Journal Title
J. Struct. Biol.
EDGE-DETECTION; RECONSTRUCTION; CRYOMICROSCOPY; Biochemistry & Molecular Biology; Biophysics; Cell Biology
Three-dimensional reconstruction of large macromolecules like viruses at resolutions below 10 Angstrom requires a large set of projection images. Several automatic and semi-automatic particle detection algorithms have been developed along the years. Here we present a general technique designed to automatically identify the projection images of particles. The method is based on Markov random field modelling of the projected images and involves a pre-processing of electron micrographs followed by image segmentation and post-processing. The image is modelled as a coupling of two fields-a Markovian and a non-Markovian. The Markovian field represents the segmented image. The micrograph is the non-Markovian field. The image segmentation step involves an estimation of coupling parameters and the maximum A posteriori estimate of the realization of the Markovian field i.e, segmented image. Unlike most current methods, no bootstrapping with an initial selection of particles is required. (C) 2003 Elsevier Inc. All rights reserved.
Journal of Structural Biology
Article; Proceedings Paper
"Image segmentation for automatic particle identification in electron micrographs based on hidden Markov random field models and expectation maximization" (2004). Faculty Bibliography 2000s. 4806.