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

G Harauz·A Fong-Lochovsky
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Abstract

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.

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AlgorithmsEscherichia coliImage Processing, Computer-AssistedMicroscopy, ElectronMitosporic FungiRibosomesSoftwareJournal ArticleResearch Support, Non-U.S. Gov't

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BibTeXRIS

G Harauz, A Fong-Lochovsky. 1989. Automatic selection of macromolecules from electron micrographs by component labelling and symbolic processing.. https://doi.org/10.1016/0304-3991(89)90331-8

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