The library also contains a large family of operators based on geodesic reconstruction ( Soille, 2003), such as hole filling or automated identification of extended minima or maxima. Grayscale attribute opening allows efficient filtering of images based on a size criterion. 2.1 Image processing and filteringĪ large collection of pre-processing operations (noise reduction, edge detection, feature enhancement…) is available through tunable morphological filters (closing, opening, top-hat, morphological gradients…).
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#IMAGEJ PLUGINS MANUAL#
The complete list of operators is given in the user manual ( Supplementary File S1).
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The library provides different categories of functions, corresponding to standard image processing workflows. MorphoLibJ implements a large collection of MM algorithms applicable to 2D and 3D grey level images and integrated into user-friendly interfaces. To meet the needs for generic implementations and ImageJ interfaces of MM operators, we developed the GPL-licensed MorphoLibJ library.
#IMAGEJ PLUGINS SOFTWARE#
However, such software are specific to a particular application, and rarely interact with ImageJ. Many software have emerged for image analysis of plant tissues ( Barbier de Reuille et al., 2015 Fernandez et al., 2010 Pound et al., 2012 Schmidt et al., 2014). Understanding the cellular bases of complex phenomena such as plant development and morphogenesis requires quantitative approaches of cellular morphology and tissue architecture. However, none of them implements a comprehensive and consistent set of MM operators, supporting 2D as well as 3D images, and compatible with arbitrary image types (8, 16 and 32 bits). Several plugins include morphological filtering ( Landini, 2008 Ollion et al., 2013 Prodanov et al., 2006), or segmentation ( Tsukahara 2008, available at ) tools. One of the most popular bioimage informatics tools is the ImageJ/Fiji software, which offers a large spectrum of image processing operators and allows addition of plugins developed by the community ( Schindelin et al., 2012). Though it constitutes a methodology of choice for the processing of biological images, it is not widely used, possibly due to the lack of developer-friendly implementations and user-friendly interfaces. It extends naturally to the processing of three dimensional images. Typical applications comprise image filtering and enhancement, segmentation and analysis. Mathematical morphology (MM) is a class of image processing algorithms and methods with well-established theoretical foundations that have proven useful for a large variety of problems ( Soille, 2003). There is thus a recurrent need for efficient image processing algorithms and for user-friendly software that integrates them.
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Biological images present specific difficulties for automated analysis, due to data dimensionality (space, time, channels, etc.), acquisition artefacts, or low contrast. The continuous development of microscopy imaging techniques results in increasingly better spatial resolution, signal quality and throughput and allows 3D imaging of biological cells, tissues or organisms.