Simulation of microbiological objects fluorescent images

The use of digital technology in analyzing the properties of cells and their substructures. Modeling of synthetic images, allowing to determine the properties of objects and the measuring system. Creation of luminescent images of microbiological objects.

Рубрика Биология и естествознание
Вид реферат
Язык английский
Дата добавления 19.04.2017

The Ministry of Education of the Republic of Belarus

The Belarusian State University

The English Language Department for Sciences

Simulation of microbiological objects fluorescent images

CONTENTS

  • Abstract
  • Аннотация
  • Introduction
  • Simulation of microbiological objects fluorescent images
  • Conclusion
  • Bibliography
  • Glossary

ABSTRACT

Key words: confocal microscopy, modelling, automatic analysis, cells, microbiological objects, cancer.

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. 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].

It 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].

This 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.

The 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].

Each 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.

The 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.

However, 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.

One 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].

Markers 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.

The 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.

After 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.

Due to various biological causes cells can be combined into clusters. The determined number Nc of clusters is evenly randomly distributed in the image with the coordinates (xc,yc). 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 pc and distributed uniformly randomly with probability (1-pc) [2, 3].

The 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.

The 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.

Image 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.

Not 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].

To 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].

Thus, 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.

The 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.

To 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.

Figure 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.

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.

Visual 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 ч2 goodness of fit was used to verify the quality of modelling and showed that the values did not exceed critical values of ч2 at a significance level of 0.95 indicating that the statistical conditions of ч2 were satisfied.

Moreover, the equivalent radii of nuclei on the experimental image were compared with those on the simulated synthetic image. The ч2 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 ч2 values for 19 degrees of freedom gave 9.61 which was less than the critical value of ч2 equal to 10.1 at a significance level of 0.95.

During 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.

As 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).

CellPainter 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.

The 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.

By 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.

In 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.

The 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.

In 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

5. 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.

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

GLOSSARY

1.

aberration

аберрация

2.

absorption

поглощение

3.

acquisition

получение

4.

adaptive boosting

адаптивное стимулирование

5.

adjacent cells

смежные клетки

6.

algorithm

алгоритм

7.

analytical representation

аналитическое представление

8.

angular gradient

угловой градиент

9.

application

приложение

10.

approach

подход

11.

approximation

аппроксимация

12.

arrangement

расположение

13.

artifact

артефакт

14.

artificial image

искусственное изображение

15.

automated analysis

автоматический анализ

16.

background

фон

17.

basic components

основные компоненты

18.

bias

смещение

19.

bioinformatics

биоинформатика

20.

bleeding

испускание

21.

blurring

размытие

22.

border

граница

23.

bound

граница

24.

calculation

вычисление

25.

cancer

рак

26.

cell

клетка

27.

cell activity

клеточная активность

28.

cell types

типы клеток

29.

channel

канал

30.

chemotaxis

хемотаксис

31.

chromosome

хромосома

32.

classification

классификация

33.

cluster

кластер

34.

color space

цветовое пространство

35.

combination

сочетание

36.

composite

смесь

37.

compression

сжатие

38.

computer graphics

компьютерная графика

39.

concentration

концентрация

40.

configuration

конфигурация

41.

confocal microscope

конфокальный микроскоп

42.

conformity

соответствие

43.

constraint

ограничение

44.

contamination

загрязнение

45.

continuity

непрерывность

46.

contour roughness

неровность контура

47.

contrast

контрастность

48.

conversion

конвертирование

49.

convex hull

выпуклая оболочка

50.

convolution

конволюция

51.

coordinate

координата

52.

correlation

корреляция

53.

correspondence graph

граф соответствия

54.

criterion

критерий

55.

curvature

кривизна

56.

cytometry

цитометрия

57.

cytoplasm

цитоплазма

58.

dark-field microscopy

темнопольная микроскопия

59.

data mining

интеллектуальный анализ данных

60.

deconvolution

деконволюция

61.

deficiency

нехватка

62.

deformation

деформация

63.

degradation

вырождение

64.

demonstration

демонстрация

65.

dependence

зависимость

66.

deployment

размещение

67.

depth

глубина

68.

detection

выявление

69.

deviation

отклонение

70.

digital camera

цифровая камера

71.

digital signal

цифровой сигнал

72.

dilatation

дилатация

73.

dimension

размерность

74.

direction

направление

75.

discrete signal

дискретный сигнал

76.

displacement

смещение

77.

distinction

различие

78.

distortion

искажение

79.

distribution

распределение

80.

DNA

ДНК

81.

dye

краситель

82.

dynamics

динамика

83.

edge

край

84.

efficiency

эффективность

85.

eigenvalue

собственное значение

86.

electron microscopy

электронный микроскоп

87.

emission

эмиссия

88.

empirical evidence

эмпирическое свидетельство

89.

emulation

эмулирование

90.

enumeration

перечисление

91.

equalization

выравнивание

92.

equation

уравнение

93.

equidistant sampling

равноудаленная выборка

94.

equivalent radius

эквивалентный радиус

95.

erosion

эрозия

96.

Eulerian formulation

формулировка Эйлера

97.

evaluation

оценка

98.

expression

экспрессия

99.

extension

расширение

100.

extreme point

точка экстремума

101.

factor

фактор

102.

feature

характерная черта

103.

filtration

фильтрация

104.

fine structure

тонкая структура

105.

fitting

подгонка

106.

flexible contour

гибкий контур

107.

fluorescence

флуоресценция

108.

flux

поток

109.

focal plane

фокальная плоскость

110.

focus

фокус

111.

Fourier series expansion

разложение в ряд Фурье

112.

frame

кадр

113.

framework

фреймворк

114.

gene

ген

115.

generation

генерация

116.

goodness of fit

критерий согласия

117.

graphic user interface

графический пользовательский интерфейс

118.

grayscale image

полутоновое изображение

119.

halfspace

полупространство

120.

hierarchical clustering

иерархическая кластеризация

121.

high-speed pipeline

высокоскоростной источник информации

122.

histogram

гистограмма

123.

hyperbolic manifold

гиперболическое множество

124.

hyperplane

гиперплоскость

125.

identification

опознавание

126.

image

изображение

127.

image mask

маска изображения

128.

image pre-processing

предобработка изображений

129.

image registration

регистрация изображения

130.

impulse

импульс

131.

incremental algorithm

пошаговый алгоритм

132.

indicator

индикатор

133.

infrared analysis

инфракрасный анализ

134.

input

входные данные

135.

intensity

интенсивность

136.

intercellular contacts

внутриклеточные контакты

137.

interference

интерференция

138.

interpolation

интерполяция

139.

intersection

пересечение

140.

irregularity

неравномерность

141.

iteration

итерация

142.

kernel

ядро

143.

k-means clustering

кластеризация методом k-средних

144.

label

метка

145.

Lagrangian formulation

формулировка Лагранжа

146.

laser

лазер

147.

light source

источник света

148.

light-field microscopy

светлопольная микроскопия

149.

limitation

ограничение

150.

linear gradient

линейный градиент

151.

link

связь

152.

localization

локализация

153.

location

расположение

154.

locomotion

передвижение

155.

luminescence

люминесценция

156.

machine learning

автоматическое обучение

157.

mapping

отображение

158.

marker

маркер

159.

matrix

матрица

160.

mean

среднее значение

161.

measurement system

измерительная система

162.

merging

слияние

163.

microarray

микромассив

164.

migration

перемещение

165.

misalignment

смещение

166.

modeling

моделирование

167.

morphology

морфология

168.

motility

подвижность

169.

movement

движение

170.

multichannel representation

многоканальное представление

171.

multispectral video

многоспектральное видео

172.

naked eye

невооруженный глаз

173.

neighbourhood

соседство

174.

nondegenerate solution

невырожденное решение

175.

nucleus

ядро

176.

numerical aperture

числовая апертура

177.

numerical method

численный метод

178.

objective

объектив

179.

objective function

целевая функция

180.

observation

наблюдение

181.

octave

октава

182.

optical diffraction

оптическая дифракция

183.

organelle

органелла

184.

orientation

ориентация

185.

outlier

выброс

186.

outline

контур

187.

output

выходные данные

188.

overlap

перекрывание

189.

oversampling

передискретизация

190.

package

пакет

191.

parameter

параметр

192.

parametric model

параметрическая модель

193.

pattern

шаблон

194.

pattern recognition

распознавание образов

195.

Perlin noise

шум Перлина

196.

persistence

стойкость

197.

phase

фаза

198.

photoactivation

фотоактивация

199.

photobleaching

фотообесцвечивание

200.

photomultiplier

фотоэлектронный умножитель

201.

pixel

пиксель

202.

plasticity

гибкость

203.

platform

платформа

204.

plugin

плагин

205.

polar angle

полярный угол

206.

polarity

полярность

207.

polytope

многогранник

208.

population

популяция

209.

precision

точность

210.

probe

проба

211.

proximity

близость

212.

quality characteristics

качественные характеристики

213.

quantification

квантование

214.

queue

очередь

215.

quickhull partitioning

разбиение методом быстрых оболочек

216.

random model

произвольная модель

217.

random permutation

случайная перестановка

218.

random polygon

произвольный полигон

219.

randomization

рандомизация

220.

randomness

случайность

221.

range

диапазон

222.

ratio

отношение

223.

ray

луч

224.

reconstruction

реконструкция

225.

recovery

восстановление

226.

refraction

рефракция

227.

regulation

регулирование

228.

resolution

разрешение

229.

restoration

восстановление

230.

RNA

РНК

231.

robustness

прочность

232.

sampling frequency

частота дискретизации

233.

scale

шкала

234.

section

раздел

235.

segment

сегмент

236.

segmentation

сегментация

237.

sensitivity

чувствительность

238.

sequence

последовательность

239.

signal-noise ratio

отношение сигнал/шум

240.

simplification

упрощение

241.

simulation

симуляция

242.

smoothness

гладкость

243.

Sobel operator

оператор Собеля

244.

software

программное обеспечение

245.

solution convergence

сходимость решения

246.

source code

исходный код

247.

spatial location

пространственное расположение

248.

specification

спецификация

249.

spectrometry

спектрометрия

250.

spline

сплайн

251.

splitting

расслаивание

252.

spot

пятно

253.

spread

распространие

254.

staining

окрашивание

255.

stationery sensor

неподвижный датчик

256.

statistical conditions

статистические условия

257.

statistics

статистика

258.

stream

поток

259.

subcellular components

субклеточные компоненты

260.

subpopulation

субпопуляция

261.

successive stages

последовательные стадии

262.

supervised learning

контролируемое обучение

263.

surface

поверхность

264.

symmetry

симметрия

265.

synthetic image

синтетическое изображение

266.

tag

метка

267.

target

цель

268.

technique

техника

269.

temporal variation

временное отклонение

270.

tensor

тензор

271.

texture

текстура

272.

thickness

толщина

273.

three-dimensional structure

трехмерная структура

274.

threshold

порог

275.

throughput

пропускная способность

276.

tissue

ткань

277.

topological flexibility

топологическая гибкость

278.

tracking

отслеживание

279.

trajectory

траектория

280.

transfer

перенос

281.

transformation

преобразование

282.

treatment

обращение

283.

triangulation

триангуляция

284.

tumor

опухоль

285.

turbulent noise

турбулентный шум

286.

usage scenario

пользовательский сценарий

287.

validation

подтверждение

288.

value

величина

289.

variance

дисперсия

290.

vector

вектор

291.

verification

верификация

292.

versatility

гибкость

293.

vertice

вершина

294.

viewing condition

условия просмотра

295.

visual appearance

внешнее представление

296.

visualization

визуализация

297.

Voronoi diagram

диаграмма Вороного

298.

watershed segmentation

сегментация методом водоразделов

299.

wavelet

вейвлет

300.

weighted sum

взвешенная сумма

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