Journal of Pathology Informatics Journal of Pathology Informatics
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RESEARCH ARTICLE
Year : 2012  |  Volume : 3  |  Issue : 1  |  Page : 25

ImageJS: Personalized, participated, pervasive, and reproducible image bioinformatics in the web browser


1 Division Informatics, Department of Pathology, University of Alabama at Birmingham, Alabama, USA
2 Division Informatics, University of Alabama at Birmingham, Alabama, USA
3 Division Informatics, Department of Pathology and Biomedical Engineering, University of Alabama at Birmingham, Alabama, USA
4 Division Informatics, Department of Pathology and Electrical and Engineering, University of Alabama at Birmingham, Alabama, USA

Correspondence Address:
Jonas S Almeida
Division Informatics, Department of Pathology, University of Alabama at Birmingham, Alabama
USA
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© 2012 Almeida et al; This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


DOI: 10.4103/2153-3539.98813

PMID: 22934238

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Background: Image bioinformatics infrastructure typically relies on a combination of server-side high-performance computing and client desktop applications tailored for graphic rendering. On the server side, matrix manipulation environments are often used as the back-end where deployment of specialized analytical workflows takes place. However, neither the server-side nor the client-side desktop solution, by themselves or combined, is conducive to the emergence of open, collaborative, computational ecosystems for image analysis that are both self-sustained and user driven. Materials and Methods: ImageJS was developed as a browser-based webApp, untethered from a server-side backend, by making use of recent advances in the modern web browser such as a very efficient compiler, high-end graphical rendering capabilities, and I/O tailored for code migration. Results : Multiple versioned code hosting services were used to develop distinct ImageJS modules to illustrate its amenability to collaborative deployment without compromise of reproducibility or provenance. The illustrative examples include modules for image segmentation, feature extraction, and filtering. The deployment of image analysis by code migration is in sharp contrast with the more conventional, heavier, and less safe reliance on data transfer. Accordingly, code and data are loaded into the browser by exactly the same script tag loading mechanism, which offers a number of interesting applications that would be hard to attain with more conventional platforms, such as NIH's popular ImageJ application. Conclusions : The modern web browser was found to be advantageous for image bioinformatics in both the research and clinical environments. This conclusion reflects advantages in deployment scalability and analysis reproducibility, as well as the critical ability to deliver advanced computational statistical procedures machines where access to sensitive data is controlled, that is, without local "download and installation."


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