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共聚焦激光扫描显微镜获得的大 3D 图像的并行反卷积。

Parallel deconvolution of large 3D images obtained by confocal laser scanning microscopy.

机构信息

Department of Computer Science, Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, AGH University of Science and Technology, Kraków.

出版信息

Microsc Res Tech. 2010 Mar;73(3):187-94. doi: 10.1002/jemt.20773.

Abstract

Various deconvolution algorithms are often used for restoration of digital images. Image deconvolution is especially needed for the correction of three-dimensional images obtained by confocal laser scanning microscopy. Such images suffer from distortions, particularly in the Z dimension. As a result, reliable automatic segmentation of these images may be difficult or even impossible. Effective deconvolution algorithms are memory-intensive and time-consuming. In this work, we propose a parallel version of the well-known Richardson-Lucy deconvolution algorithm developed for a system with distributed memory and implemented with the use of Message Passing Interface (MPI). It enables significantly more rapid deconvolution of two-dimensional and three-dimensional images by efficiently splitting the computation across multiple computers. The implementation of this algorithm can be used on professional clusters provided by computing centers as well as on simple networks of ordinary PC machines.

摘要

各种去卷积算法常用于数字图像的恢复。共聚焦激光扫描显微镜获取的三维图像需要特别进行去卷积校正。这些图像存在失真,特别是在 Z 维度上。因此,对这些图像进行可靠的自动分割可能很困难,甚至不可能。有效的去卷积算法需要大量的内存和时间。在这项工作中,我们提出了一种基于分布式内存的著名 Richardson-Lucy 去卷积算法的并行版本,并使用消息传递接口(MPI)实现。它通过有效地在多台计算机之间分配计算,显著加快了二维和三维图像的去卷积速度。该算法的实现可以在计算中心提供的专业集群上使用,也可以在普通 PC 机的简单网络上使用。

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