The field of biological imaging has evolved considerably during the past decade as a result of recent (r)evolutions in fluorescence labeling and optical microscopy. Bioimage informatics has been identified as a top priority to cope with the ever-increasing amount of microscopy data. The challenges and opportunities for researchers in image and signal processing are manyfold. They span the areas of mathematical imaging, with problems such as denoising, 3-D deconvolution and super-resolution localization, as well as image analysis for the segmentation, detection and recognition of biological structures in 3-D. The dynamic aspect of the data requires the development of novel algorithms for tracking fluorescent particles and analyzing high-throughput microscopy data (labeling of cells, phenotyping, extraction of gene expression profiles). A crucial aspect of bioimage informatics is making image analysis tools available to biologists so that they can be applied to real data and used on a routine basis. Developers may benefit from open-source frameworks and international initiative such as OBIA for easying-up this process and creating collaboration networks with biologists.