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Processing large images can only be done by loading portions of them into memory, one at a time, until the complete image has been treated. ImageJ-and almost any other generic biomedical image processing software-is made for processing images that can be fully loaded in the main memory, with limitations coming from both the memory management side and the internal data structures (e.g., with a defined maximum number of pixels) since are not aimed at very large images. The advent of digital slides is however posing one major issue, correlated with the size of the images which could be in the order of hundreds of Mpixels. Among those areas, microscopy-originated images have been one of ImageJ targets since the very beginning, although normally processing occurs on selected microscopic fields, instead of the whole slide. By means of its extensions, ImageJ may become a platform that can be specialized for usage in specific image processing and analysis areas.
#IMAGEJ SOFTWARE NATURE PAPER UPDATE#
This eventually led to the birth of a project, “Fiji”, aimed at enhancing ImageJ architecture and update mechanism. A large community of developers, which often are dedicated users with biological background, continuously enhances ImageJ by addressing bugs and particularly developing plugins and macros.
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Furthermore, plugins (external software modules that can be run from a menu or inside macros) can be also implemented using the Java language, for faster and more complex functionalities. Macros (that is, interpreted software programs written in the internal scripting language) can be programmed for implementing specific functions not already available or to combine existing functions to be automatically executed. It allows for the most important image processing and analysis procedures on a variety of image formats and sources, but it also allows for its extension and customization through add-ons such as macros and plugins. ImageJ is a well known and long-lived open source software for biomedical image analysis. In particular, image analysis over digital pathology samples is aimed at tissue classification, quantification of biomarkers, identification of rare events like mitoses, etc. As a matter of fact, under the term “digital pathology” now many digital slide applications are collected, including in particular telemedicine and image analysis. While Whole Slide Imaging can be applied in any microscopy-based area, Pathology is the specialty with most attention to digital slides. Image size is one of the reasons why they are not yet commonly used in routine. However, digital slides are acquired at resolutions up to 0.2 μ/pixel, thus consisting of up to billions of pixels. Having such samples in digital form brings many advantages such as the immutability of the sample along time as well as the possibility to analyze it more automatically using many different image analysis methods. The digital slide, or Whole Slide Image, is a digital image, acquired with specific scanners, that represents a complete tissue sample or cytological specimen at microscopic level.
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The digitalization of full glass slides containing tissue samples for microscopic analysis is a reality that is becoming stronger and opens to many opportunities.
The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. There was no additional external funding received for this study.
įunding: The work was partially supported and funded by the EU FP7 program, AIDPATH project, grant number 612471. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The software described in the paper is available as open source (LGPL license) at the address.
Received: Accepted: Published: July 6, 2017Ĭopyright: © 2017 Della Mea et al. PLoS ONE 12(7):Įditor: Helmut Ahammer, Medical University of Graz, AUSTRIA Citation: Della Mea V, Baroni GL, Pilutti D, Di Loreto C (2017) SlideJ: An ImageJ plugin for automated processing of whole slide images.
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