With the advent of digital cameras and motorization of mechanical components, computer control of microscopes has become increasingly important. Software for microscope image acquisition should not only be easy to use, but also enable and encourage novel approaches. The open‐source software package µManager aims to fulfill those goals. This unit provides step‐by‐step protocols describing how to get started working with µManager, as well as some starting points for advanced use of the software. Curr. Protoc. Mol. Biol. 92:14.20.1‐14.20.17. © 2010 by John Wiley & Sons, Inc.

μManager is an open-source, cross-platform desktop application, to control a wide variety of motorized microscopes, scientific cameras, stages, illuminators, and other microscope accessories. Since its inception in 2005, μManager has grown to support a wide range of microscopy hardware and is now used by thousands of researchers around the world. The application provides a mature graphical user interface and offers open programming interfaces to facilitate plugins and scripts. Here, we present a guide to using some of the recently added advanced μManager features, including hardware synchronization, simultaneous use of multiple cameras, projection of patterned light onto a specimen, live slide mapping, imaging with multi-well plates, particle localization and tracking, and high-speed imaging.

Extraction of blood vessel boundaries from intravascular ultrasound images is essential in the quantitative analysis of cardiovascular functions. In this study, we are presenting a completely automated procedure for determining blood vessel borders. This approach uses textural operators to separate different tissue regions and morphological processing to refine extracted contours. The method was tested in a set of 29 intravascular ultrasound images obtained in vivo. To assess the performance of the method, we have compared the automatically processed images with the manual tracings, using three different criteria: correlation coefficient, match ratio, and relative error of computed shape parameters. In both contour detection and shape parameters estimation, the proposed method yielded consistently good results. Due to its robustness and accuracy, this approach is appropriate for clinical use, whereas computational efficiency of the method facilitates low-cost implementation.

Extraction of blood vessel boundaries from intravascular ultrasound images is essential in the quantitative analysis of cardiovascular functions. In this study, we are presenting a completely automated procedure for determining blood vessel borders. This approach uses textural operators to separate different tissue regions and morphological processing to refine extracted contours. The method was tested in a set of 29 intravascular ultrasound images obtained in vivo. To assess the performance of the method, we have compared the automatically processed images with the manual tracings, using three different criteria: correlation coefficient, match ratio, and relative error of computed shape parameters. In both contour detection and shape parameters estimation, the proposed method yielded consistently good results. Due to its robustness and accuracy, this approach is appropriate for clinical use, whereas computational efficiency of the method facilitates low-cost implementation.

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