Fluorescence microscopy combined with digital imaging constructs a basic platform for numerous biomedical studies in the field of cellular imaging. As the studies relying on analysis of digital images have become popular, the validation of image processing methods used in automated image cytometry has become an important topic. Especially, the need for efficient validation has arisen from emerging high-throughput microscopy systems where manual validation is impractical. We present a simulation platform for generating synthetic images of fluorescence-stained cell populations with realistic properties. Moreover, we show that the synthetic images enable the validation of analysis methods for automated image cytometry and comparison of their performance. Finally, we suggest additional usage scenarios for the simulator. The presented simulation framework, with several user-controllable parameters, forms a versatile tool for many kinds of validation tasks, and is freely available at http://www.cs.tut.fi/sgn/csb/simcep.
High-throughput cell measurement techniques producing images of cell populations have raised a need for accurate automated image analysis methods. Validating the analysis methods used for automated cytometry is an issue yet to be solved. Manual validation, being an exhaustively laborious task, enables comparison but does not provide solution for large scale analysis. By creating a parametric model for cell shape, and simulating images of cell populations including errors and aberrations caused by the measurement system, validation of different image analysis methods is enabled. As a result, studies with large populations, where the number of cells and many other key parameters are user-tunable, can be carried out by using simulated cell population images. The cell image simulator, as well as validation case studies for segmentation and image restoration are presented.
Background: Microarray technologies have become common tools in biological research. As a result, a need for effective computational methods for data analysis has emerged. Numerous different algorithms have been proposed for analyzing the data. However, an objective evaluation of the proposed algorithms is not possible due to the lack of biological ground truth information. To overcome this fundamental problem, the use of simulated microarray data for algorithm validation has been proposed.
Shoeprints are an important source of information for criminal investigation. Therefore, an increasing number of automatic shoeprint recognition methods have been proposed for detecting the corresponding shoe models. However, comprehensive comparisons among the methods have not previously been made. In this study, an extensive set of methods proposed in the literature was implemented, and their performance was studied in varying conditions. Three datasets of different quality shoeprints were used, and the methods were evaluated also with partial and rotated prints. The results show clear differences between the algorithms: while the best performing method, based on local image descriptors and RANSAC, provides rather good results with most of the experiments, some methods are almost completely unrobust against any unidealities in the images. Finally, the results demonstrate that there is still a need for extensive research to improve the accuracy of automatic recognition of crime scene prints.
Monitoring of bacterial populations requires automated analysis tools that provide accurate cell type quantification results. Here, methods for automated image analysis and bacteria type classification are presented. The classification method employs several discriminative features, calculated from automatically segmented images, for class determination. The performance of the algorithm is evaluated with a case study where three different bacterial types are present. Moreover, the accuracy of the method is demonstrated by generating experiments of synthetic bacterial population images.
Detection and three dimensional reconstruction of cell structures from brightfield microscopy video clips using digital image processing algorithms is presented. While the confocal microscopy offers an efficient technique for three dimensional measurements, extensive and repeated measurements are still often better to be performed using permanent staining and brightfield microscopy. By processing of brightfield microscopy videos using automated and efficient digital image processing algorithms, the tedious task of manual analysis can be avoided. Our two-stage algorithm is applied for 1) cell soma detection and 2) identification of the 3D structure of entire neurons. To verify the results, we present 3D reconstructions of the detected cells.
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