PubMed · 14146547
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T DALENIUS. 1964-04-01. [COMMENTS ON THE ARTICLE ON "DATA MACHINE OPTIMIZATION ETC."].. https://pubmed.ncbi.nlm.nih.gov/14146547/
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Images acquired in a two-view digital chest examination are frequently not electronically distinguishable. As a result the lateral and posterioanterio (PA) images are often improperly positioned on a PACS work station. A series of 1998 chest radiographs (999 lateral, 999 PA or AP) were used to develop a neural network classifier. The images were down-sampled to 16 x 16 matrices, and a feed-forward neural network was trained and tested using the "leave-one-out" method. Using five nodes in the hidden layer, the neural network correctly identified 987 of the 999 test cases (98.8%) (average of six runs). The simple architecture and speed of this technique suggests that it would be a useful addition to PACS work station software. The accumulated time saved by correctly positioning the lateral and PA chest images on the work station monitors in accordance with each radiologist's hanging protocols was estimated to be about 1 week of radiologist time per year.
The electron microscope has become an important tool for determining the structure of biological materials of all kinds. Many technical advances in specimen preparation and in sophisticated methods of image analysis, initially based on optical systems but latterly on computer processing, have contributed to the development of the subject. Viruses of various kinds have often provided a convenient and appropriate test specimen. This paper describes the major technical advances and shows how viruses have had an important role in most of the developments.