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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 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].

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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