ImageJS: Personalized, participated, pervasive, and reproducible image bioinformatics in the web browser
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 speciali...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2012-01-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2012;volume=3;issue=1;spage=25;epage=25;aulast= |
| _version_ | 1818265388715081728 |
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| author | Jonas S Almeida Egiebade E Iriabho Vijaya L Gorrepati Sean R Wilkinson Alexander Grüneberg David E Robbins James R Hackney |
| author_facet | Jonas S Almeida Egiebade E Iriabho Vijaya L Gorrepati Sean R Wilkinson Alexander Grüneberg David E Robbins James R Hackney |
| author_sort | Jonas S Almeida |
| collection | DOAJ |
| description | 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." |
| first_indexed | 2024-12-12T19:50:01Z |
| format | Article |
| id | doaj.art-f2b594340d1b4434acefe0d489826e32 |
| institution | Directory Open Access Journal |
| issn | 2153-3539 2153-3539 |
| language | English |
| last_indexed | 2024-12-12T19:50:01Z |
| publishDate | 2012-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-f2b594340d1b4434acefe0d489826e322022-12-22T00:14:00ZengElsevierJournal of Pathology Informatics2153-35392153-35392012-01-0131252510.4103/2153-3539.98813ImageJS: Personalized, participated, pervasive, and reproducible image bioinformatics in the web browserJonas S AlmeidaEgiebade E IriabhoVijaya L GorrepatiSean R WilkinsonAlexander GrünebergDavid E RobbinsJames R HackneyBackground: 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."http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2012;volume=3;issue=1;spage=25;epage=25;aulast=Cloud computingimage analysiswebApp |
| spellingShingle | Jonas S Almeida Egiebade E Iriabho Vijaya L Gorrepati Sean R Wilkinson Alexander Grüneberg David E Robbins James R Hackney ImageJS: Personalized, participated, pervasive, and reproducible image bioinformatics in the web browser Journal of Pathology Informatics Cloud computing image analysis webApp |
| title | ImageJS: Personalized, participated, pervasive, and reproducible image bioinformatics in the web browser |
| title_full | ImageJS: Personalized, participated, pervasive, and reproducible image bioinformatics in the web browser |
| title_fullStr | ImageJS: Personalized, participated, pervasive, and reproducible image bioinformatics in the web browser |
| title_full_unstemmed | ImageJS: Personalized, participated, pervasive, and reproducible image bioinformatics in the web browser |
| title_short | ImageJS: Personalized, participated, pervasive, and reproducible image bioinformatics in the web browser |
| title_sort | imagejs personalized participated pervasive and reproducible image bioinformatics in the web browser |
| topic | Cloud computing image analysis webApp |
| url | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2012;volume=3;issue=1;spage=25;epage=25;aulast= |
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