Fluorescent staining is a common tool for both quantitative and qualitative assessment of pro- and eukaryotic cells sub-population fractions by using microscopy and flow cytometry. However, direct cell counting by flow cytometry is often limited, for example when working with cells rigidly adhered either to each other or to external surfaces like bacterial biofilms or adherent cell lines and tissue samples. An alternative approach is provided by using fluorescent microscopy and confocal laser scanning microscopy (CLSM), which enables the evaluation of fractions of cells subpopulations in a given sample. For the quantitative assessment of cell fractions in microphotographs, we suggest a simple two-step algorithm that combines single cells selection and the statistical analysis. To facilitate the first step, we suggest a simple procedure that supports finding the balance between the detection threshold and the typical size of single cells based on objective cell size distribution analysis. Based on a series of experimental measurements performed on bacterial and eukaryotic cells under various conditions, we show explicitly that the suggested approach effectively accounts for the fractions of different cell sub-populations (like the live/dead staining in our samples) in all studied cases that are in good agreement with manual cell counting on microphotographs and flow cytometry data. This algorithm is implemented as a simple software tool that includes an intuitive and user-friendly graphical interface for the initial adjustment of algorithm parameters to the microphotographs analysis as well as for the sequential analysis of homogeneous series of similar microscopic images without further user intervention. The software tool entitled BioFilmAnalyzer is freely available online at https://bitbucket.org/rogex/biofilmanalyzer/downloads/.

Detection and localization problem of extended small-scale objects with different shapes appears in radio observation systems which use SAR, infra-red, lidar and television camera. Intensive non-stationary background is the main difficulty for processing. Other challenge is low quality of images, blobs, blurred boundaries; in addition SAR images suffer from a serious intrinsic speckle noise. Statistics of background is not normal, it has evident skewness and heavy tails in probability density, so it is hard to identify it. The problem of extraction small-scale objects is solved here on the basis of directional filtering, adaptive thresholding and morthological analysis. New kind of masks is used which are open-ended at one side so it is possible to extract ends of line segments with unknown length. An advanced method of dynamical adaptive threshold setting is investigated which is based on isolated fragments extraction after thresholding. Hierarchy of isolated fragments on binary image is proposed for the analysis of segmentation results. It includes small-scale objects with different shape, size and orientation. The method uses extraction of isolated fragments in binary image and counting points in these fragments. Number of points in extracted fragments is normalized to the total number of points for given threshold and is used as effectiveness of extraction for these fragments. New method for adaptive threshold setting and control maximises effectiveness of extraction. It has optimality properties for objects extraction in normal noise field and shows effective results for real SAR images

Selection and classification of objects according to certain objective criteria is one of the immense tasks for automated image analysis in computer vision systems. Here we revisit a recently suggested approach to the object selection problem based on choosing the optimal threshold to select a certain subclass of objects determined by objective criteria such as size, shape etc. While our earlier investigations mainly focused on the analysis of microscopic images, here we concentrate more on remote sensing applications. Besides revisiting the previously suggested method based on the size vs threshold histogram analysis, we also suggest several additional quantities that can be effectively used to find the best threshold once the empirical histograms exhibit several characteristic peaks. Moreover, similar approach could be useful for an automatic distinction between subclasses of objects with different morphology, thus resolving not only the selection but also the classification problem.

Adaptive object selection technique based on the results of multi-threshold image processing is considered. The keynote feature of the proposed approach is the use of information about the properties of the selected object(s) at multiple threshold values a posteriori to find an optimal threshold based on the criterion that takes into account the geometric properties of the object. Multi-threshold object selection algorithms based on several scale-invariant geometric criteria such as the object perimeter elongation coefficient and the elongation coefficient of the circumscribed ellipsoid main axis are implemented and validated. The suggested characteristics help to distinguish between compact and elongated objects while remaining invariant to the image scale. The results for both synthetic and observational imagery obtained by remote sensing equipment are used for validation purposes