![]() ![]() The pipelines and images for these examples, as well as others, are available for download ( The image of yeast colonies ( Figure 1) is a plate of Hi90-strain cells plated on synthetic defined medium with 128 µg/mL fluconazole as previously described ( 5). Here we describe its use for a variety of other applications such as yeast colony counting, grid analysis, wound healing, and other visually quantifiable assays.Īll of the image analysis in this paper used the freely available CellProfiler cell image analysis software. We recently described CellProfiler's use for cell identification, cell size, intensity and texture of fluorescent stains, cell cycle distributions, and other features of individual cells in images ( 4). There are many existing software packages available for specific applications in biology, but CellProfiler accomplishes many of the same goals in one open-source program. The compiled software is freely available for Macintosh®, PC, and Unix platforms at It can accommodate adaptation to many biological objects and assays without requiring programming, due to its modular design and graphical user interface. The CellProfiler™ project was developed to address these software challenges by providing the scientific community with an easy-to-use open-source platform for automated image analysis. While users can program custom algorithms or record macros, these customized routines are challenging to adapt without knowing a programming language or interacting directly with the macro code. ![]() At the other end of the continuum, some software packages are very flexible, especially for interactive analysis of individual images. Most commercial software is proprietary, meaning that the underlying methods of analysis are hidden from the researcher. Other packages are sold with accompanying hardware for image acquisition (e.g., yeast colony counters), but these are expensive and do not allow measurement of features beyond those that are already built-in. ![]() While numerous commercial and free software packages exist for image analysis, many of these packages are designed for a very specific purpose, such as cell counting ( 2). Efforts to automate visual analysis in biology began several decades ago, but many aspects still need improvement ( 1). This liberates biologists for more interesting work and has several advantages over visual observations including speed, quantitative and reproducible results, and simultaneous measurement of many features in the image. Certain repetitive tasks in visual analysis are suitable for automation by collecting digital images and processing them with image analysis software. While nothing can fully replace the expertise of a trained biologist, observing many samples by eye is time-consuming, subjective, and nonquantitative. One of the most powerful methods in biology is the visual analysis of a sample. This free, easy-to-use software enables biologists to comprehensively and quantitatively address many questions that previously would have required custom programming, thereby facilitating discovery in a variety of biological fields of study. Small numbers of images can be processed automatically on a personal computer, and hundreds of thousands can be analyzed using a computing cluster. The software automatically identifies objects in digital images, counts them, and records a full spectrum of measurements for each object, including location within the image, size, shape, color intensity, degree of correlation between colors, texture (smoothness), and number of neighbors. The applications demonstrated here include yeast colony counting and classifying, cell microarray annotation, yeast patch assays, mouse tumor quantification, wound healing assays, and tissue topology measurement. Here we describe the use of the open-source software, CellProfiler™, to automatically identify and measure a variety of biological objects in images. Careful visual examination of biological samples is quite powerful, but many visual analysis tasks done in the laboratory are repetitive, tedious, and subjective. ![]()
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