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.

Automated image analysis provides a powerful tool when quantifying various characteristics of cell populations. Previously, the validation of image analysis results has been a task of expert biologist, who has manually analyzed the images and provided the ground truth to which the proposed analysis results have been compared. The traditional validation approach, prone to errors and variation, is unfeasible in the emergence of high-throughput measurement systems which make human-based analysis excessively laborious. The systems biology approach for studying, e.g., cellular activity massively in parallel, often lending on high-throughput microscopy, further increases the need for efficient, validated computational methods. As a solution for the problem, we propose a computational framework for simulating fluorescence microscopy images of cell populations. The simulation framework allows generation of synthetic images with realistic characteristics including the ground truth for validation. Thus, the simulation enables validation and performance analysis for various analysis algorithms. By creating a parameterized model of cells based on a given population, the simulator is able to create different cell types. The proposed modular framework, combined with the ability to create high-throughput measurements, provides a powerful tool for validating image analysis methods in traditional microscopy as well as in high content screening. Moreover, we use experimental data to study the validity of the proposed modeling approach.

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.

MOTIVATION: Although numerous algorithms have been developed for microarray segmentation, extensive comparisons between the algorithms have acquired far less attention. In this study, we evaluate the performance of nine microarray segmentation algorithms. Using both simulated and real microarray experiments, we overcome the challenges in performance evaluation, arising from the lack of ground-truth information. The usage of simulated experiments allows us to analyze the segmentation accuracy on a single pixel level as is commonly done in traditional image processing studies. With real experiments, we indirectly measure the segmentation performance, identify significant differences between the algorithms, and study the characteristics of the resulting gene expression data. RESULTS: Overall, our results show clear differences between the algorithms. The results demonstrate how the segmentation performance depends on the image quality, which algorithms operate on significantly different performance levels, and how the selection of a segmentation algorithm affects the identification of differentially expressed genes. AVAILABILITY: Supplementary results and the microarray images used in this study are available at the companion web site http://www.cs.tut.fi/sgn/csb/spotseg/

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. RESULTS: We present a microarray simulation model which can be used to validate different kinds of data analysis algorithms. The proposed model is unique in the sense that it includes all the steps that affect the quality of real microarray data. These steps include the simulation of biological ground truth data, applying biological and measurement technology specific error models, and finally simulating the microarray slide manufacturing and hybridization. After all these steps are taken into account, the simulated data has realistic biological and statistical characteristics. The applicability of the proposed model is demonstrated by several examples. CONCLUSION: The proposed microarray simulation model is modular and can be used in different kinds of applications. It includes several error models that have been proposed earlier and it can be used with different types of input data. The model can be used to simulate both spotted two-channel and oligonucleotide based single-channel microarrays. All this makes the model a valuable tool for example in validation of data analysis algorithms.

Perceptual image quality correlates ineffectively with the traditional error measures. By defining the factors behind perceived image quality, many image processing systems can be optimized. Thus, a great deal of effort has been made for solving this challenging issue. However, considerably less research has been focused on the role of spatial resolution and number of gray levels in subjective image quality. In our study, the relation between these two fundamental image parameters is experimentally defined in terms of perceptual quality. The study was carried out by organizing an experiment where 80 subjects were used. Using the collected data set, the results are illustrated in the form of isopreference curves. Finally, our results are compared with the corresponding ones presented previously. Although the comparison reveals a clear correlation between the results, novel information about the relation between spatial resolution and the number of gray levels is presented.