Quantitating the cell: turning images into numbers with ImageJ
Quantitating the cell: turning images into numbers with ImageJ
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DOI：
10.1002/wdev.260
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发表时间：
2017-03-01
影响因子：
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通讯作者：
Eliceiri, Kevin W.
中科院分区：
文献类型：
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作者：
Arena, Ellen T.;Rueden, Curtis T.;Eliceiri, Kevin W.
现代生物学研究，特别是在发育和细胞生物学领域，已经被光学显微镜的快速发展所改变。光学显微镜，长期以来一直是实验生物学家的支柱，现在用于各种生物实验场景和样品类型。先进生物成像的许多重大发展都是由具有强大处理器和算法的数字成像革命推动的。特别是，这种先进的成像和计算分析的结合导致了现代生物学家不仅在视觉上检查动态现象，而且量化所涉及的过程。这种对定量图像的需求已经成为生物成像领域的主要推动力，并且需要具有相应直观软件包的可扩展和可访问的图像处理例程。新的算法都是专门为光学显微镜或从其他领域，如天文学，适应生物学家，但往往是在一种形式，是无法访问的数据输入问题，可用性和培训问题，以及可访问性和输出限制的原因。生物界对这一需求作出了回应，开发了开放源码软件包，这些软件包可免费获得，并提供图像处理程序。其中最突出的是开源图像包ImageJ。在这篇评论中，我们给出了一个概述突出的成像处理方法在ImageJ，我们认为是特别感兴趣的生物成像，并说明ImageJ和其他开源图像分析软件的功能。(C)2016 Wiley Periodicals，Inc.
Modern biological research particularly in the fields of developmental and cell biology has been transformed by the rapid evolution of the light microscope. The light microscope, long a mainstay of the experimental biologist, is now used for a wide array of biological experimental scenarios and sample types. Much of the great developments in advanced biological imaging have been driven by the digital imaging revolution with powerful processors and algorithms. In particular, this combination of advanced imaging and computational analysis has resulted in the drive of the modern biologist to not only visually inspect dynamic phenomena, but to quantify the involved processes. This need to quantitate images has become a major thrust within the bioimaging community and requires extensible and accessible image processing routines with corresponding intuitive software packages. Novel algorithms both made specifically for light microscopy or adapted from other fields, such as astronomy, are available to biologists, but often in a form that is inaccessible for a number of reasons ranging from data input issues, usability and training concerns, and accessibility and output limitations. The biological community has responded to this need by developing open source software packages that are freely available and provide access to image processing routines. One of the most prominent is the open-source image package ImageJ. In this review, we give an overview of prominent imaging processing approaches in ImageJ that we think are of particular interest for biological imaging and that illustrate the functionality of ImageJ and other open source image analysis software. (C) 2016 Wiley Periodicals, Inc.