AUTOMATIC SELECTION OF MACROMOLECULES FROM ELECTRON-MICROGRAPHS BY COMPONENT LABELING AND SYMBOLIC PROCESSING
AUTOMATIC SELECTION OF MACROMOLECULES FROM ELECTRON-MICROGRAPHS BY COMPONENT LABELING AND SYMBOLIC PROCESSING
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DOI：
10.1016/0304-3991(89)90331-8
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发表时间：
1989-12-01
期刊：
影响因子：
2.2
通讯作者：
FONGLOCHOVSKY, A
中科院分区：
文献类型：
--
作者：
HARAUZ, G;FONGLOCHOVSKY, A
描述了从电子显微照片中提取单个生物大分子的图像的新解决方案。该过程中有三个不同的步骤。低级图像处理的初始阶段包括噪声抑制和边缘检测。组件标记和特征计算的中间阶段桥接了标志性（低级）处理与符号（高级）处理的最后阶段之间的差距。简单的符号对象（边界框）是从边缘得出的，并且在决策过程中很容易表示和操纵。使用核糖体和肋骨亚基的电子显微照片证明了该算法的功效。分析的层次结构体现了数据的减少和其性质的变化。最初，必须处理数千个连续灰度的像素。在组件标签后，只有不到一百个边界的盒子可以很容易地由专家定义和阐明。因此，已编写的软件包可以作为将人工智能方法应用于电子显微照片的分析的基础。
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 ribsomal subunits. The hierarchical nature of the analysis embodies a reduction in the amount of data and a change in its nature. Initially, thousands of pixes 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.