Web-based 3D medical image visualization framework for biomedical engineering education

Journal article

Authors/Editors

TIRANEE ACHALAKUL

Strategic Research Themes

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Publication Details

Author list: Settapat S., Achalakul T., Ohkura M.

Publisher: Wiley

Publication year: 2014

Journal: Computer Applications in Engineering Education (1061-3773)

Volume number: 22

Issue number: 2

Start page: 216

End page: 226

Number of pages: 11

ISSN: 1061-3773

eISSN: 1099-0542

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899485019&doi=10.1002%2fcae.20548&partnerID=40&md5=6656140988856eb80a42279e35590cc1

Languages: English-Great Britain (EN-GB)

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Abstract

Medical imaging is one of the major fields in the multi-disciplinary curriculum of biomedical engineering (BME). Biomedical engineers from different backgrounds need to understand biology in order to be able to develop effective equipment to improve healthcare diagnosis through medical images. Visualization tools are important in the learning process to improve biomedical engineer's understanding of medical imagery. In this work, we design a web-based 3D medical image visualization framework that can be used to improve medical image understanding in biology and anatomy. Our proposed framework provides not only 3D visualization, but also 3D reconstruction for medical images. This paper describes the design framework and the technology integration, as well as the implementation details. The developed system has been used as an educational tool prototype in a BME department. In order to evaluate the tool's usability, we tested it with BME students as well as doctors. We compared a conventional 2D visualization application and our proposed method with regard to system efficiency and user satisfaction. The 3D system generally demonstrated better performance and a higher level of satisfaction. The students were able to use our 3D tool to study 2D images effectively without prior background knowledge in anatomy. ฉ 2011 Wiley Periodicals, Inc.

Keywords

3D visualization, Distance Learning, medical image visualization, X3D