A visualization environment for electron microscopy

Abstract

Describes an environment for visualization and processing of low-dose, low-contrast electron micrographs of biological specimens. We focus on image selection, the first step in the process of reconstruction of the 3D structure of a specimen from its projections. Noise from a variety of sources makes automatic detection of particle positions a difficult task. New image acquisition devices and modern electron microscopy methods require the processing and rendering of very large images (50-100 million pixels). We describe techniques for processing large images, algorithms for detecting particle positions on compressed images using the crosspoint method, and methods for position refinement. EMMA, an interactive visualization environment for experimenting with particle identification methods is presented.