Arganda-Carreras Ignacio, Andrey Philippe
Ikerbasque, Basque Foundation for Science, 48013, Bilbao, Spain.
Computer Science and Artificial Intelligence Department, Basque Country University (UPV/EHU), 20018, Donostia-San Sebastian, Spain.
Methods Mol Biol. 2017;1563:185-207. doi: 10.1007/978-1-4939-6810-7_13.
With the progress of microscopy techniques and the rapidly growing amounts of acquired imaging data, there is an increased need for automated image processing and analysis solutions in biological studies. Each new application requires the design of a specific image analysis pipeline, by assembling a series of image processing operations. Many commercial or free bioimage analysis software are now available and several textbooks and reviews have presented the mathematical and computational fundamentals of image processing and analysis. Tens, if not hundreds, of algorithms and methods have been developed and integrated into image analysis software, resulting in a combinatorial explosion of possible image processing sequences. This paper presents a general guideline methodology to rationally address the design of image processing and analysis pipelines. The originality of the proposed approach is to follow an iterative, backwards procedure from the target objectives of analysis. The proposed goal-oriented strategy should help biologists to better apprehend image analysis in the context of their research and should allow them to efficiently interact with image processing specialists.
随着显微镜技术的进步以及所获取的成像数据量的迅速增长,生物学研究中对自动化图像处理和分析解决方案的需求日益增加。每个新应用都需要通过组合一系列图像处理操作来设计特定的图像分析流程。现在有许多商业或免费的生物图像分析软件,并且有几本教科书和综述介绍了图像处理和分析的数学及计算基础。已经开发了数十种(甚至数百种)算法和方法并将其集成到图像分析软件中,这导致可能的图像处理序列出现组合爆炸式增长。本文提出了一种通用的指导方法,以合理地处理图像处理和分析流程的设计。所提出方法的独特之处在于从分析的目标出发遵循一种迭代的、反向的过程。所提出的面向目标的策略应有助于生物学家在其研究背景下更好地理解图像分析,并应使他们能够与图像处理专家进行高效互动。