Simulation of microbiological objects fluorescent images

The Ministry of Education of the Republic of BelarusBelarusian State UniversityEnglish Language Department for Sciences

of microbiological objects fluorescent images

CONTENTS

Abstract

Аннотацияof microbiological objects fluorescent images

ABSTRACT

Key words: confocal microscopy, modelling, automatic analysis, cells, microbiological objects, cancer.success of digital technologies in image acquisition has promoted the development of automatic cytometry - cells and their substructures properties analysis. The efficiency and robustness of automatic analysis algorithms may be improved by modelling synthetic images, which allows defining basic features of objects and the measurement system. This paper proposes a simulation algorithm and its practical implementation to create fluorescent images of microbiological objects. The comparison of generated and experimental cancer tumors images confirms their similarity, which allows using the developed method to study and debug algorithms.

АННОТАЦИЯ

Ключевые слова: конфокальная микроскопия, моделирование, автоматический анализ, клетки, микробиологические объекты, рак.

Успехи применения цифровой техники при получении изображений способствовали развитию автоматической цитометрии - анализа свойств клеток и их подструктур. Повысить эффективность и устойчивость алгоритмов автоматического анализа может моделирование синтетических изображений, позволяющее определить основные свойства объектов и измерительной системы. В данной работе предложен алгоритм моделирования и его реализация для создания люминесцентных изображений микробиологических объектов. Результаты сравнения полученных изображений раковых опухолей с экспериментальными подтверждают их схожесть, что позволяет использовать предложенный метод при исследовании и отладке алгоритмов.

INTRODUCTION

The success of digital technologies in image acquisition has promoted the development of automatic cytometry - cells and their substructures properties analysis. The efficiency and robustness of automatic analysis algorithms may be improved by modelling synthetic images, which allows defining basic features of objects and the measurement system [1]. Varying simulation parameters allows one to study robustness of automatic analysis algorithms to different influences which appear in the process of image acquisition and to define the most effecting factors during experiments [2].is a complex process to simulate images with parameters similar to real features. Nevertheless, basic features of objects and the measurement system can be studied when some real objects characteristics are neglected. Furthermore, cell simulation is not possible without simplifications [2].paper proposes a simulation algorithm and its practical implementation to create fluorescent images of microbiological objects. It has allowed producing a list of fluorescent images of cancer tumors. The statistical analysis was carried out to check the model significance. The comparison of generated and experimental images confirms their similarity, which allows using the developed method to study and debug algorithms.obtained images enable to reveal qualitative morphological system properties. They can be used to measure certain tissue areas characteristics. A wide possible simulation parameters list provides generating diverse sets of images.

SIMULATION OF MICROBIOLOGICAL OBJECTS FLUORESCENT IMAGES

While modelling the process of image obtaining is divided into successive stages corresponding to a real experimental procedure using a fluorescence microscope. At the first stage an ideal image is generated which consists of specially labelled cells. The simulation result at this stage is an ideal object. Then the obtained image is distorted due to measurement system errors: uneven illumination of the object, background autofluorescence, optical errors, noise from the photomultiplier, etc. Thus, the output is an image which has properties similar to real fluorescent images [3].type of cell is defined independently by an appropriate form of cells and their organelles, as well as by sets of markers that define the texture and colour of these forms. There can be set dependences between subpopulations which affect the position of cells, their shape and markers [4]. Then the effects of errors of the measurement system and the generated ideal image may overlay.first step towards obtaining synthetic images of cell populations is to define these populations and the objects they include. These objects are cells that may contain nuclei, cytoplasm, lipids and other components. To generate shapes of cells and their organelles a parametric model is used. The shape is defined as a polygon with a given number of vertices and then the position of certain vertices is modified. The final shape is obtained by smoothing the contour using cubic spline interpolation., the peculiarity of this model is that the shape of each object is generated independently, thus it is necessary to specify the correspondence between objects belonging to the same cell. It is possible due to the definition of dependences while setting generation parameters [3]. For example, in order to make sure that the nucleus gets inside the corresponding cytoplasm, one should specify the dependence between this nucleus and the cytoplasm that has already been defined. Otherwise, one can create the shape of a nucleus independently, but one will have to determine the dependence on this nucleus when setting the parameters of the cytoplasm. Thus, there are two types of generated shapes: independent from other objects and associated ones, the position of which is determined by independent objects. For each of subpopulations only one type of objects is independent while the others should be determined.more important factor is how the generated form will look like. At least one marker able to describe the appearance of the object is required to solve this problem. The feature of this simulation model is that objects can be determined not only by some value of intensity of given colour and texture, but by a number of markers as well. This makes it possible to create visually complex objects and to generate images very similar to the real ones [3].definition occurs sequentially, thus it is important to take into consideration their order. Therefore, all these operations can be divided into two groups. The first group includes operations establishing the basic level of the object marker intensity. These markers should be set primarily [3]. This group includes: a) the marker of a constant intensity level that sets the same intensity for all pixels of the object based on the Gaussian distribution; b) the marker the intensity of which is constant, but depends on the density of objects in the surrounding area; c) the marker which sets a linear dependence of a constant intensity level on the intensity of another marker in the specified area; d) the marker which sets the intensity according to the position of the object on the image in which random texture for the whole image is determined first and then the average value for the object is calculated.second group of markers consists of those operations that do not determine the level of intensity themselves, but only redistribute their value. That is why these markers should be applied only after definition of baseline intensity using markers of the first group. The examples of markers changing the intensity level are angular and linear gradients in any direction; markers that define the intensity depending on the proximity of borders and other cell organelles; markers which specify texture with the help of Perlin [2, 5] and turbulent noise [6]. One more marker in the second group is the marker which can scale the intensity level in a given range, which may be very useful for enhancing image contrast.defining the objects properties separately it is important to consider the parameters that characterize the whole population. These parameters include the number and arrangement of cells.to various biological causes cells can be combined into clusters. The determined number N_c of clusters is evenly randomly distributed in the image with the coordinates (x_c,y_c). Cells assigned to a cluster are arranged around the center of the cluster according to exponential distribution. Thus, cells will be combined into a cluster with probability p_c and distributed uniformly randomly with probability (1-p_c) [2, 3].level of different objects overlapping in this simulation model is defined as a set of rules that specify possible values of objects overlapping. The introduction of a number of rules allows not only acquiring images similar to real experimental ones, but is strongly needed when working with such a variety of subpopulations and their objects. Another characteristic related to overlapping is visibility of objects markers that are compared with the cell. This model provides defining weight coefficients that determine fractions of the object and the cell.final stage of the modelling process is to distort the generated ideal image by effects which are observed in a real measurement system. Within this simulation model such effects as image illumination distortion, optical aberrations, noise from the photomultiplier and improper cells staining when labelling [7] may be observed.illumination distortion is usually caused by the influence of a light source which leads to an increase in the image intensity. This results in contrast reduction and displacement of a light source can introduce additional problems in segmenting objects [6]. In this model image illumination distortion is defined as an increase of illumination intensity of each image point by a certain value. Uneven image illumination can also be modelled. In this case image illumination can be represented by a linear gradient in any direction or by a radial gradient with the center of a light source at a random point.all objects are located at the focal plane of the microscope because of three-dimensional structure of examined samples. This results in blurring some objects. To add a blurring effect two-dimensional Gaussian blurring is used in this simulation model which allows transferring data contained in pixels using Gaussian distribution to the outer zone [5]. This effect is observed as a result of the Gaussian filter with oversampling - a process of changing sampling frequency of a discrete (usually digital) signal [5].generate fluorescent images cells are labelled with special dyes called fluorophores. However, this treatment may make adjustments to the final image which is obtained with a microscope. For example, some cells may not be well processed by the substance, while others can absorb an unexpectedly large amount of a dye [7]., this simulation model allows generating images of cell populations, including many different types of cells. Taking into consideration the dependencies between the synthesized objects allows better recreating the real picture. In fact, cells and their organelles have a tremendous impact on lives of each other, which is reflected in the experimental images obtained using a fluorescence microscope and is the direct object of study. The determined simulation parameters make it possible to obtain a large number of different images from the viewpoint of cells and cell populations morphology. A new approach when setting markers allows generating diverse cellular populations of a complex visual representation, which is a big advantage of this simulation model. However, a comparatively short list of opportunities for modelling experimental conditions a little bit restricts the field where this model can be implemented.simulation algorithm of modelling fluorescent images of microbiological objects is based on the theory described earlier. It corresponds to the classical approach in modelling fluorescent images of cellular systems, i.e. the process of obtaining an image is divided into successive stages that occur in the real experiment. Thus, the first step is to create an ideal image of the cell population. The result is then deliberately distorted according to the impact of the measurement system and the environment [1, 2]. As a result of these actions the final synthetic image is obtained.generate an ideal image it is necessary to determine all objects of a given population, to define how these objects will look and be placed on the image. After completing these steps for each of the simulated subpopulations it is possible to move on to the second stage - the introduction of distortion. These stages are shown in Figure 1 which is worth considering in more detail.

1. Block diagram of the simulation algorithm

Stage 1. Defining population objects. The first stage in the process of simulating luminescent images of cellular systems is the determination of all the objects constituting the system. For each of the subpopulations the objects and relationships between them must be set. Each cell organelle must be linked with a specific cell or its nucleus, which in practice is achieved by directly specifying an anchor of the object. The definition of each cell or its objects shape using a parametric model with polygons also takes place at this stage.2. Defining markers. The appearance of the generated shapes is determined by a set of markers for each object. This approach helps to create a texture and visual representation of the cell as a collection of various transformations applied to the basic marker of the cellular object.

Stage 3. Population location. Location of cells within the simulated image of the cellular population may be uniform random, but in real life cells are much more likely to be grouped in clusters. Assigned to the cluster cells will be located near the predetermined cluster centers, while other objects will be evenly distributed over the rest of the space.

Stage 4. Defining overlapping rules. Once all the parameters of the cellular system subpopulations are defined it is necessary to determine the interaction between these subpopulations. For this purpose a number of overlapping rules between the objects are defined. Overlaps can occur between the same objects at the object level of one and the same or different subpopulations.

Stage 5. Merging populations. The stage of merging populations is transparent to a user and does not require direct involvement. After determining all the objects of the cell population their placement in the final image takes place.

Stage 6. Measurement system errors. This is the final step for the entire modelling process. Imposition of distortions introduced by the measurement system and the environment is held at this stage. The output of this stage is a generated resulting image of the cell population with all possible errors taken into consideration.

Software that allows generating fluorescent images of microbiological objects was obtained as a result of realization of an appropriate simulation algorithm. Figure 2 shows some examples of the obtained images.similarity of experimental and generated synthetic images is not enough to ensure adequacy of the developed simulation model and its compliance with real experimental images. That is why numerical comparison of the available experimental images of cancer tumors and reproduced synthetic images was drawn.

Figure 2. Simulated synthetic images

digital technology microbiological objects

The analysis of the intensity histograms of the affected cells nuclei on simulated and experimental images in three colour channels was conducted. The results showed similarity of the images intensity. The χ² goodness of fit was used to verify the quality of modelling and showed that the values did not exceed critical values of χ² at a significance level of 0.95 indicating that the statistical conditions of χ² were satisfied., the equivalent radii of nuclei on the experimental image were compared with those on the simulated synthetic image. The χ² goodness of fit was used again for the objects distribution histogram according to the value of their equivalent radii to check their conformity with the laws of distribution. The calculation of χ² values for 19 degrees of freedom gave 9.61 which was less than the critical value of χ² equal to 10.1 at a significance level of 0.95.the process of cancer tumor cells modelling several simulation parameters varied. This provided an opportunity to examine how the simulated image changed depending on the errors of the measurement system. Measurement system illumination, optical aberrations that lead to blurring of registered objects, uneven labelling by fluorophores and photomultiplier noise were chosen as variable parameters. Thus, changing some simulation parameters allows reaching a wide variety of modelled images, which plays a very important role due to a great amount of possible experimental conditions.a result of simulation model practical realization the software package called CellPainter was implemented for simulating fluorescent images of microbiological objects. This package includes the simulation algorithm itself, as well as a graphical user interface that makes it possible to greatly simplify the software application (Figure 3).provides two different types of interface. The first type of interface is designed to work with a numerical description of the model parameters (mode User 1), while the second type of interface allows users to select values of the model parameters in accordance with the submitted sample (mode User 2). However, the range of options when working in user mode 2 is limited and covers only the most important stages of modelling.

Figure 3. Mode User 1 basic form

CONCLUSION

As the result of this work a simulation model and an image simulation algorithm have been developed, the primary purpose of which is to simulate fluorescent images of microbiological objects.developed software package makes it easy to simulate the necessary synthetic image due to the implementation of two graphical user interfaces. When working in mode User 1 all simulation parameters have to be entered in numerical form and for mode User 2 a more user-friendly graphical interface is implemented: one can select parameters on the basis of the samples offered, but the range of options is limited and covers only the most important stages of modelling.means of the implemented application it became possible to reproduce a number of various microbiological objects fluorescent images, including a series of cancer cells images. To verify adequacy and consistency of the model the equivalent radii of the affected cells on the experimental and generated images, as well as the intensity levels in different colour channels of image elements were compared.spite of the differences from the experimental images the obtained synthetic images can reveal qualitative morphological properties of the system and allow measuring individual characteristics of the simulated tissue and measuring system. Moreover, a vast list of possible simulation parameters provides a possibility to generate a wide variety of images.developed simulation model and application implemented on its basis provide successful simulation of different biological objects fluorescent images. At the same time the software has a convenient and user-friendly interface.the future on the basis of simulation approaches models that characterize not only the location of cell populations but also their state should be considered. There is a need to develop a model describing biological processes in the cell, and to implement a model of an interacting cells layer which are not thoroughly studied yet.

BIBLIOGRAPHY

1. Feofanov, A.V. Spectral laser scanning confocal microscopy in biological research / A.V. Feofanov // Advances of Biological Chemistry / RAC; ed. L.P. Ovchinnikova - Moscow, 2007. - V. 47, P. 371-400.

2.      Computational framework for simulating fluorescense microscope images with cell populations / A. Lehmussola [et al.] // IEEE transactions on medical imaging. - 2007. - Vol. 26, №7. - P. 1010-1016.

3.      SimuCell: a flexible framework for creating synthetic microscopy images / S. Rajaram [et al.] // Nat Methods. - 2012. - №9. - P. 634-635.

4.      Altschuler&Wu Lab [Electronic resourse] / UT Southwestern Medical Center. - Dallas, 2014

.        Gonzalez, R. Digital image processing using MATLAB / R. Gonzalez, R. Woods, S. Eddins. - Moscow : Technosphere, 2006. - 616 p.

6. Lisitsa, Y. Fully-automated segmentation of tumor nuclei in canсer tissue images / Y.Lisitsa [et al.] // Pattern recognition and information processing, Minsk, 18-20 May 2011 / BSUIR - Minsk, 2011. - P. 116-120.

.   Karnaukhov, V.N. Fluorescent analysis of cells / V.N. Karnaukhov; ed. A.Y. Budanceva - Puschino: Analytical microscopy, 2002. - 130 p.

GLOSSARY