Features of daime
 
Batch import and processing. Series of images, which belong to the same experiment, or confocal z-stacks can be imported in a single step into daime. Subsequently, all images that belong to the same series or stack can be edited, segmented, analyzed, and visualized together. Thus, even hundreds of images can be handled with only a few mouse clicks. Of course, daime imports and analyzes single images, too.
When colour images are imported, the RGB colour channels are automatically separated. This feature simplifies the evaluation of FISH experiments using multiple oligonucleotide probes: the individual colour channels, which contain different probe-labelled microbial populations, can be edited, segmented and analyzed separately.
Image segmentation. Once imported into daime, images can be prepared for segmentation (object recognition) for example by applying noise reduction and background removal algorithms. daime can automatically detect 2D and 3D objects in single images, image series, and confocal image stacks. The program offers various segmentation methods, which range from simple manual threshold selection to complex local thresholding and edge detection algorithms. An interactive object editor offers powerful tools to manually refine the results of automatic image segmentation. Features of individual objects (brightness, surface, area, volume, etc.) can be measured directly in the object editor. The objects in image series or confocal image stacks can also be measured automatically (batch processing).
Abundance quantification of microorganisms. Research in microbial ecology often requires that the in situ abundance of microbial populations is quantified. daime greatly facilitates this tedious task as it can measure the relative abundances (fractions of the total biovolume) of probe-labelled populations in digital images. This approach works with planktonic cells as well as with cell aggregates embedded in biofilms.
Analysis of spatial localization patterns. The spatial arrangement of microorganisms, for example in biofilms and flocs, can reflect their biological interactions. Mutualistic symbionts may co-aggregate, whereas antagonists may rather avoid each other. Spatial localization cannot reliably be analyzed by simple microscopic observation, which lacks statistical support and bears the risk of overlooking subtle arrangement patterns. daime offers methods for quantifying the spatial localization of microbes in confocal images. By using these features, one can determine whether the spatial arrangement of probe-labelled microbial populations differs from a random distribution and whether the organisms do or do not co-aggregate.
Evaluation of rRNA-targeted probes. The specificity of rRNA-targeted oligonucleotide probes is greatly affected by the hybridization conditions (the stringency). To determine the optimal stringency for FISH with a new probe, one usually performs FISH experiments with probe-target and non-target organisms and increasing amounts of formamide in the hybridization buffers. daime can automatically evaluate such formamide series by measuring the brightness of the probe-labelled microbial cells in digital images. This feature yields a probe dissociation profile after only a few mouse clicks.

[click to enlarge]		 3D visualization. Confocal microscopy comes to life when it is combined with adequate 3D visualization software. Those visualization functions, which are bundled with the microscope vendors′ imaging programs, do often not meet the expectations of users who want to interactively explore their 3D data on screen. daime overcomes these limitations and offers a high-speed volume rendering engine that was designed to run on current commodity PCs and graphics hardware (including laptops). Rendering features include free rotation/zooming/panning and popular visualization modes such as direct volume rendering and maximum intensity projection. The program can also render shadows and semi-transparent surfaces, extract isosurfaces, perform 3D clipping, and display stereo anaglyphs at interactive frame rates. Showing the 3D arrangement of different biofilm populations is straightforward, because several image stacks can easily be combined in the same 3D scene.