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Medical Imaging Informatics: An Overview

  • Euclid Seeram
Chapter
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Abstract

Imaging informatics (II) has replaced the term medical imaging informatics (MI) and is now commonplace in the imaging community. The Society of Imaging Informatics in Medicine (SIIM) states that the “science of imaging informatics is the study and application of processes of information and communications technology for the acquisition, manipulation, analysis, and distribution of image data.” This chapter describes the essential technologies such as the fundamentals of computers and communication technologies that are the building blocks for an understanding of II. In addition, relevant elements of the picture archiving and communication systems (PACS), the radiology information system (RIS), and the electronic health record (EHR) are highlighted in terms of definitions and components. Furthermore, an overview of system integration and information technology security is provided. Finally, emerging topics of II such as cloud computing, Big Data, artificial intelligence, machine learning, and deep learning are briefly described. Since the latter four topics, Big Data, artificial intelligence, machine learning, and deep learning, are in their infancy in terms of development and implementation, major definitions of each have been quoted from the experts, so as not to detract from the original meaning.

Big Data is characterized by four Vs: volume, variety, velocity, and veracity. While volume refers to the very large amount of data, variety deals with a wide array of data from multiple sources. Furthermore, velocity addresses the very high speeds at which the data is generated. Finally, veracity describes the uncertainty of the data such as the authenticity and credibility. The definitions for AI, machine learning, and deep learning are as follows (see appropriate citations in the text):

  • AI is the “effort to automate intellectual tasks normally performed by humans.”

  • Machine learning is “a set of methods that automatically detect patterns in data, and then utilize the uncovered patterns to predict future data or enable decision making under uncertain conditions.”

  • Deep learning algorithms are “characterized by the use of neural networks with many layers.”

As these emerging topics in imaging informatics evolve, they will gain acceptance and become useful tools in medical imaging technologies.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Euclid Seeram
    • 1
    • 2
    • 3
    • 4
    • 5
  1. 1.Medical Radiation Sciences University of SydneySydneyAustralia
  2. 2.Medical Radiation Sciences, Faculty of Health SciencesUniversity of SydneySydneyAustralia
  3. 3.Adjunct Associate Professor, Medical Imaging and Radiation SciencesMonash UniversityClaytonAustralia
  4. 4.Adjunct Professor, Faculty of ScienceCharles Sturt UniversityWagga WaggaAustralia
  5. 5.Adjunct Associate Professor, Medical Radiation Sciences, Faculty of HealthUniversity of CanberraBruceAustralia

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