2012
DOI: 10.4103/2153-3539.98813
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ImageJS: Personalized, participated, pervasive, and reproducible image bioinformatics in the web browser

Abstract: 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 f… Show more

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Cited by 39 publications
(37 citation statements)
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“…(C) Immunohistochemical staining of Rhbdf2 +/+ and Rhbdf2 P159L/P159L skin sections with proliferation marker Ki-67. Quantification of proliferating cells was performed as described previously (Almeida et al, 2012). Scale bars: 100 μm.…”
Section: Resultsmentioning
confidence: 99%
“…(C) Immunohistochemical staining of Rhbdf2 +/+ and Rhbdf2 P159L/P159L skin sections with proliferation marker Ki-67. Quantification of proliferating cells was performed as described previously (Almeida et al, 2012). Scale bars: 100 μm.…”
Section: Resultsmentioning
confidence: 99%
“…Moreover images could be annotated not only with a basic description and technical details, but also with terms from fourteen different ontologies in sixteen different biological fields. In the second contribution [35], Almeida et al presented a browser-based webApp aiming to develop open, collaborative, computational ecosystems for image analysis that are both self-sustained and user driven. This application, named as ImageJS, contains segmentation, feature extraction and image filtering modules and it ultimately aims to provide a pervasive mechanism to deliver image analysis workflows and interactive interfaces to where the data are.…”
Section: B Computational Steps In Bioimage Informaticsmentioning
confidence: 99%
“…The merits of the "serverless" approach have been well understood, and have been applied to biomedical data for a number of years, from genomics 5 to image analysis in Pathology 6 . However, until recently it came with the suspicion that either the analytical challenge could not computationally intensive, or that a dedicated server-side indexing resource would have to help carry the load.…”
Section: Introductionmentioning
confidence: 99%