Integrative Open-Source Software for Image Analysis in Biology

Imaging for evaluating and displaying biological structures and dynamic processes is making more and more demands on image analysis software. The quantification of thousands of microscopic images, sample screenings, fluorescence lifetime measurements and many other biological investigations creates a proliferation of image data requiring objective and reproducible evaluation. Therefore, increasing use is being made of segmentation, object tracking, machine learning or visualization algorithms. Widely used open-source solutions that support this include 3D Slicer [9], BioImageXD [10], CellProfiler [11], Fiji [8], FluoRender [12], Icy [13], Image Surfer [14], IMOD [15], KNIME [7], OsiriX [16] and Reconstruct [17].

In this context, ImageJ (originally NIH Image) plays a special role among open-source programs for biological image processing [2, 18–20]. It is the most widely known and frequently used bioimage analysis tool. One of the main reasons for its success is its modular design, realized by a plug-in mechanism. This plug-in mechanism not only allows the compilation of user-specific ImageJ versions, but also enables software developers to design algorithms without particularly detailed knowledge of the native ImageJ programming interfaces. Several hundreds of plug-ins and macros have been published in the last few years for solving a wide variety of image analysis problems.

However, it can be difficult for end users to decide on the right ImageJ plug-in for their specific application as there are so many to choose from and many are only marginally different. For this reason, Fiji offers a version of ImageJ that is tailored to the needs of the bioimaging community in that it has special plug-ins and macros for analyzing microscope images. These plug-ins can be easily and transparently managed via an integrated update mechanism.

Another well-known and flexible open-source image analysis software program is CellProfiler [11]. Complex image processing pipelines can be mapped by linking different modules. Many different problems have already been solved with the software, especially in the field of high-content screening.

The further analysis, evaluation and publication of image analysis results often calls for visual editing of both statistic data or processed image data. The sheer choice of visualization options for potentially multidimensional and sometimes extremely large image data alone requires special methods and software [21].

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