Machine Vision and Applications (2012) 23:603–605 DOI 10.1007/s00138-012-0436-2 EDITORIAL Special issue on microscopy image analysis for biomedical applications Stephen J. McKenna · Derek Magee · Nasir M. Rajpoot Published online: 8 June 2012 © Springer-Verlag 2012 Demand for tools that extract quantitative information from microscopy images of biological samples continues to grow. This is providing interesting challenges for computer vision researchers. Typical tasks are detection, segmentation, clas- sification, motion analysis, and tracking of cells and subcel- lular compartments. Image data are acquired using various imaging technologies, each with its own characteristics. In the six papers presented in this special issue these include brightfield [1], phase contrast [7], electron [5], and fluores- cence [2, 3, 6] microscopy. There is also considerable vari- ation in the biological samples to be analysed. Here these include plant roots, cervical cancer cells and neuronal den- drites, for example. These papers were selected and revised from submis- sions received in response to an open call for papers. Three of them [1, 5, 6] were developed from presentations at the symposium on microscopy image analysis for biomedical applications that we chaired in April 2010. The meeting was organised by the British Machine Vision Association (BMVA) and held at the British Computer Society in Lon- don. A further paper based on a talk given at that meeting, on nuclei detection using support vector machines, has also been published recently in this journal [4]. S. J. McKenna (B ) School of Computing, University of Dundee, Dundee DD1 4HN, UK e-mail: stephen@computing.dundee.ac.uk D. Magee School of Computing, University of Leeds, Leeds LS2 9JT, UK e-mail: d.r.magee@leeds.ac.uk N. M. Rajpoot Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK e-mail: n.m.rajpoot@warwick.ac.uk Automatic analysis of adherent mammalian cells in bright- field images is a challenging task, not least because of their low contrast. It is a problem that has arguably received rel- atively little attention in the literature. Ali et al. [1] present methods for detection and segmentation of adherent cells in brightfield images using phase and texture features. As a side benefit, their framework allows registration of cells in bright- field and fluorescence images. They evaluate their methods by comparison to manual and fluorescence-based segmen- tation. Their software sephaCe is open-source and has been made freely available. Esteves et al. [3] also address detection and segmentation but in a rather different setting: analysis of cell nuclei in con- focal fluorescence images of Arabidopsis thaliana root tips. They demonstrate that an improved gradient convergence fil- ter method allows shape priors to be imposed on this largely bottom-up process. This improves nucleus detection in sce- narios where cells appear overlapping or in close proximity. Sethuraman et al. [6] also analyse confocal fluorescence images of A. thaliana but are concerned with tracking cell membranes rather than segmenting nuclei. These membranes appear in a given two-dimensional slice as a network struc- ture. Their contribution is to propose a Markov chain Monte Carlo sampling scheme for tracking this structure using net- work snakes. Theriault et al. [7] segment, classify and track mouse fibro- blast cells imaged using phase-contrast microscopy. They employ a battery of standard shape and appearance-based features to characterise their morphological state. They use AdaBoost to classify each cell instance into one of eight clas- ses based on spread, polarization and orientedness attributes. They also show that the performance of their simple tracking algorithm increases as they use the classification algorithm to filter out substrate clutter. Expert-labeled datasets for time- lapse phase-contrast microscopy images of cells are scarce, 123