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Related Experiment Video

Updated: Jun 18, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Computing human image annotation.

David S Channin1, Pattanasak Mongkolwat, Vladimir Kleper

  • 1Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA. dsc@northwestern.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
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The Annotation and Image Markup (AIM) project standardizes image annotations and markup for cancer research. This enables querying and integrating imaging data into broader data mining efforts.

Area of Science:

  • Biomedical Informatics
  • Medical Imaging
  • Data Science

Background:

  • Current clinical and research imaging practices store annotations in proprietary formats, hindering data retrieval and analysis.
  • Radiology reports often contain annotations in free text, making them difficult to query and integrate with other data mining efforts.

Purpose of the Study:

  • To describe the National Cancer Institute's Annotation and Image Markup (AIM) project.
  • To demonstrate how AIM facilitates querying image annotations.
  • To enable the integration of human image observations into data mining and analysis.

Main Methods:

  • Developed an information model for image annotation and markup using controlled terminologies.
  • Harmonized AIM model classes and attributes with other National Cancer Institute (NCI) models and common data elements.

Related Experiment Videos

Last Updated: Jun 18, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

  • Created XML schemata for AIM instantiation and a software tool for translating AIM XML to DICOM S/R and HL7 CDA.

    • Main Results:

    • AIM provides a standardized, human- and machine-readable format for image annotations and markup.
    • AIM enables the creation of queryable collections of annotations as Grid or Web services.
    • AIM facilitates the inclusion of image observations and inferences in data mining activities.

    Conclusions:

    • The AIM project offers a robust solution for managing and querying image annotations in biomedical research.
    • Standardized annotations improve data interoperability and facilitate advanced data mining and analysis.
    • AIM supports the integration of qualitative image interpretation into quantitative research frameworks.