Image segmentation for automatic particle identification in electron micrographs based on hidden Markov random field models and expectation maximization.
Three-dimensional reconstruction of large macromolecules like viruses at resolutions below 10 A 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 á 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.
Singh
Vivek
V
School of Computer Science, University of Central Florida, Orlando, FL 32816, USA. vsingh@cs.ucf.edu
Marinescu
Dan C
DC
Baker
Timothy S
TS
D000465:Algorithms; D016880:Anisotropy; D017122:Bacteriophage T4 / Q000737:chemistry / Q000648:ultrastructure; D020285:Cryoelectron Microscopy / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008390:Markov Chains; D010363:Pattern Recognition, Automated; D012398:Ross River virus / Q000737:chemistry / Q000648:ultrastructure; D014771:Virion / Q000737:chemistry / Q000648:ultrastructure