Automatic selection of macromolecules from electron micrographs by component labelling and symbolic processing

A new solution to the problem of extracting images of individual biological macromolecules from electron micrographs is described. There are three distinct steps in the process. The initial stage of low-level image processing consists of noise suppression and edge detection. An intermediate stage of component labelling and feature computation bridges the gap between the iconic (low-level) processing and the final phase of symbolic (high-level) processing. Simple symbolic objects (bounding boxes) are derived from the edges, and are easily represented and manipulated in the decision-making process. The efficacy of the algorithm is demonstrated using electron micrographs of ribosomes and ribosomal subunits. The hierarchical nature of the analysis embodies a reduction in the amount of data and a change in its nature. Initially, thousands of pixels of continuous gray levels must be dealt with. After component labelling, there are fewer than a hundred bounding boxes whose manipulation can easily be defined and articulated by an expert. The software package that has been written can thus serve as a basis for applying artificial intelligence methodologies to analysis of electron micrographs.