Читать реферат по биологии: "Simulation of microbiological objects fluorescent images" Страница 1

назад (Назад)скачать (Cкачать работу)

Функция "чтения" служит для ознакомления с работой. Разметка, таблицы и картинки документа могут отображаться неверно или не в полном объёме!

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


Интересная статья: Быстрое написание курсовой работы