Document Type

Article

Publication Title

Informatics in Medicine Unlocked

Publication Date

2019

Keywords

web rendering, medical information, information extraction, WebGL2, internet, information sharing, medical data handling, Voxel classification, distributed disease diagnosis

Abstract

Network based medical data computing and collaborative visualization have been commonly used in remote medicine and distributed diagnosis, where visualizing 3D medical data on web browsers and sharing medical information on internet are critically important. However, due to the lack of efficient algorithms and compatible graphics hardware support, there are still some major technical challenges in web based medical data visualization and information exploration on internet. To address these practical issues, we created a new network based medical data rendering and information sharing system, where an Apache HTTP Server was applied to handle data information, and MySQL and PHP were exploited for data storage and management. In this system, medical data rendering and computation were supported with GPU and WebGL 2.0 (WebGL2), and a novel data information extraction algorithm was designed to optimize data storage and management. Taking advantage of the new 3D features of WebGL2, a web based raycasting algorithm was developed to deliver real-time data visualization on web browsers, in which a novel voxel classification method was integrated for color mapping and high-quality image generation.

The developed medical information system can deliver 60 ± 2 frames per second rendering performance for high-quality medical data visual exploration on modern browsers as well as medical information communication on internet. The system has been seamlessly integrated with web server, database and client computers equipped with modern graphics hardware, which has wide applications in the fields such as internet based computer-aided medical decision and education, as well as distributed disease diagnosis.

Funding Source

The author would like to thank the Faculty Startup Funds and Grant for Professional Development of the School of Information Technology at the Illinois State University. The author also thanks the Publication Incentive Program and Publication Open Access Grant of the Illinois State University as well as the support and collaborations of his colleagues. As an adjunct faculty, the author would appreciate the support of the Department of Medical Biophysics at the Western University for providing digital library access and research collaborations. Finally, the author would like to thank Dr. Peters' lab in the Robarts Research Institute, Western University for supplying part of the medical data sets used in this research project.

Comments

First published in Informatics in Medicine Unlocked 14 (2019) 69–81. https://doi.org/10.1016/j.imu.2018.10.010.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

DOI

10.1016/j.imu.2018.10.010

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