DAIME: Digital Image Analysis in Microbial Ecology

daime is a scientific image analysis and visualization program for microbiology and microbial ecology. It contains numerous tools for analyzing 2D and 3D microscopy datasets of microorganisms that were labelled by fluorescence in situ hybridization or other fluorescence staining techniques. daime analyzes single planktonic cells as well as cell aggregates in many kinds of samples, including biofilms.


daime has already been used in hundreds of studies in microbial ecology, medical microbiology, microbiome research, and environmental engineering. Its 3D visualization features were used to render the cover illustrations of "Brock - Biology of Microorganisms" (12th edition) and of PNAS vol. 103(7).

All functions of daime are fully integrated and accessible through a convenient graphical user interface. No programming skills are required to use daime.

Free for use in academia

daime is free for use in academic research and education.

How to get daime

daime is available for Windows and Linux operating systems. Mac users may run daime for Windows or Linux in a virtual machine.

Download daime here!

Selected Program features

daime Reference

Daims H, Lücker S, Wagner M. 2006. daime, a novel image analysis program for microbial ecology and biofilm research. Environ. Microbiol. 8: 200-213. PubMed

daime is an acronym for "digital image analysis in microbial ecology". The program is written in C++ and developed by Holger Daims.

Analysis of single 2D images, batches of images, and 3D z-stacks

daime works with single images as well as with batches of multiple images that must be analyzed together (e.g., replicate images for quantitative analyses). Confocal image stacks (z-stacks), which contain 3D image data, can also be imported and analyzed. Most functions of daime are adapted for 2D and 3D image processing and analysis.

Working with image batches greatly facilitates the evaluation of large image datasets: literally hundreds of images can be analyzed with just a few mouse clicks, and the results are automatically summarized by the program.

2D and 3D Image segmentation

Image segmentation (object recognition, for example of microbial cells and cell aggregates) is a crucial but often challenging step of image analysis. daime can detect 2D objects in single images and image batches, and 3D objects in z-stacks. It contains features for fully automatic, semi-automatic, and manual image segmentation. Objects are recognized based on their fluorescence intensity, edges, and color.



daime contains a powerful object editor that is fully integrated in the 2D and 3D visualization module. Here, users can interactively select objects for all kinds of analyses that daime can perform. For example, the 3D algorithms allow users to virtually "walk through" a 3D z-stack and pick 3D objects by point-and-click with the mouse.


Various features of 2D and 3D objects can be measured. This includes object size, brightness, surface area, volume, and other parameters. Segmented images are also used for microbial abundance quantification, spatial arrangement pattern analyses, and the other image analysis features.


Connection and data exchange with R

The R language and environment for statistical computing and graphics is a popular platform for all kinds of data analyses. Although daime is a stand-alone software, it can optionally be linked with an existing R installation for enhanced data plotting and analysis. Once established this connection is seamless, and users can enjoy the joint power of daime and R. No R programming skills are required, but users who have them will benefit even more: daime can generate R scripts for reproducing and fine-tuning its results plots directly in R, and it can export all measured data in tabular formats that make it easy to import the data into R for custom analyses.

Quantification of Microbial abundance

daime counts objects, such as microbial cells and cell clusters, in 2D and 3D images. In addition, it can quantify the biovolume fraction of a population (relative to the total biovolume of all microbes) from batches of 2D-segmented images. One image batch shows a specific FISH probe signal, and the other batch shows the signal of the general bacterial probe mix or a suitable nucleic acid stain. Notably, this stereological approach estimates a 3D parameter (biovolume fraction) from 2D images. It does not require the time-consuming acquisition of z-stacks. Biovolume fractions are often more informative than cell numbers, because biovolume represents the "biochemical reaction space" of a microbial population. Just consider that few large cells may have the same biovolume as many small cells!

Quantification of spatial arrangement patterns

The spatial arrangement (co-aggregation or avoidance) is a key feature of microbes in biofilms and other spatially structured samples. It can point to important biological interactions between microorganisms, such as mutualistic symbioses, competition for resources, and even predator-prey relationships.



Spatial arrangement patterns can be complex and subtle, and it is difficult and unreliable to evaluate them only by visual observation in the microscope. daime offers a suite of unique stereological algorithms that quantify the spatial arrangement of microbial populations, which have been labeled by specific FISH probes or other fluorescent markers. All approaches work with batches of 2D-segmented images and with 3D-segmented z-stacks.

Virtual slicing of stratified biofilms

Stratification of biofilms, flocs, or granules often reflects the ecophysiological requirements of the microorganisms in the different depth zones. Not only the abundance, but also spatial arrangement patterns and other features of microbial populations may differ between the layers of a stratified biofilm. To quantify stratification-related phenomena, daime can virtually slice biofilm images. Once the images are sliced, all image analysis functions of daime can be applied to characterize the microbial populations in each depth zone.

The automatic image slicing algorithm offers much flexibility. The direction of slicing, thickness of the sections, and other parameters can be adjusted. Up to four slicing directions can be combined, so that even spherical or tube-shaped structures can be analyzed.

Melting curve analysis of FISH probes

When a new rRNA-targeted FISH probe is designed, the optimal stringency conditions for specific hybridization must be determined experimentally. Usually this is done in a series of FISH experiments with target and non-target organisms and increasing formamide (FA) concentrations in the hybridization buffers. daime offers a special option to evaluate such FA series. It measures the fluorescence intensities of the cells in a batch of images, which were taken after FISH at the different FA concentrations. The probe dissociation profile (melting curve) is then determined from the mean fluorescence intensities.

Interactive 2D and 3D visualization

The full power of confocal microscopy is unleashed when z-stacks are projected in 3D for interactive exploration. Have you ever dived into your microbial samples? daime offers high-speed 3D visualization tools that render z-stacks in real time. Free rotation and virtual "fly-through" are easily possible, and multiple z-stacks can be combined in the same 3D scene. Rendering parameters can be freely adjusted to visualize the important features of 3D datasets. Hidden structures are exposed by semi-transparent surface rendering or by clipping, and the 3D impression is improved by virtual lighting and shadows. Stereo anaglyphs (for red-green glasses) can be rendered at interactive frame rates and popular display modes, such as maximum intensity projection, are also included.

daime can produce eye-catching still images, but movie creation is also easy: just define a few keyframes, and daime calculates the whole animation sequence between them. Rendered images and animations can be exported as image or movie files for use in presentations and publications.