METHOD FOR DETERMINING PARTICLES

Abstract

A method serves for determining particles (3), in particular bacteria in fluid and operates using an imaging optical device with a light source (1), with an optical sensor (4) with a field of light-sensitive pixels and with a fluid sample, which is to be examined, arranged between the light source (1) and the sensor (4). Characteristics of at least one particle (3), which is detected with regard to imaging, are compared to characteristics of a characteristics collection for determining the detected particle (3). The image acquisition is effected with darkfield technology and a light-sensitive pixel comprises several subpixels which are used for image acquisition.

Claims

1. A method for determining particles, in fluid, the method comprising: providing an imaging optical device comprising with a light source, an optical sensor with field of light-sensitive pixels; arranging a fluid sample which is to be examined, between the light source and the sensor; image acquisition with the imaging optical device detecting imaging characteristics of the at least one particle which is detected; comparing the detected imaging characteristics to characteristics of a characteristics collection, for determining the detected particle; effecting the image acquisition with darkfield technology, and at least one of the light-sensitive pixels comprises several subpixels which are used for the image acquisition.

2. A method according to claim 1, wherein the at least one particle is detected by way of two-dimensional black-and-white images in different planes of the fluid sample.

3. A method according to claim 1, wherein the subpixels are used for increasing the resolution or the sensitivity of the sensor or for increasing both the resolution and the sensitivity of the sensor.

4. A method according to claim 1, wherein at least one pixel of the sensor comprises several subpixels, of which at least one is high gained and at least one is low gained.

5. A method according to claim 1, wherein at least three, different characteristics of a particle are used for a detection thereof.

6. A method according to claim 1, wherein the extension of a particle with regard to area in the image, in which the particle is in focus, is used as a characteristic of a particle.

7. A method according to claim 6, wherein a pixel limit value is fixed for detecting an extension of the particle with regard to area and all pixels with a pixel value that is larger or equal to a fixed pixel limit value are set to 1, and all pixels with a pixel value that is smaller than the fixed pixel limit value are set to 0, whereupon the extension of the particle with regard to area is determined.

8. A method according to claim 7, wherein the extension of the particle with regard to area is effected based on several differently fixed pixel limit values.

9. A method according to claim 1, wherein a rotation of a particle about an axis of the particle is determined as a characteristic of the particle.

10. A method according to claim 1, wherein a characteristic of a particle is effected by evaluating a series of images of the particle in different planes, with which the particle in some images lies in focus and in some images lies out of focus, wherein a number of the pixel values representing the particle is detected in a picture-wise manner and a distribution of the detected numbers over the number of images forms the characteristic.

11. A method according to claim 10, wherein a standard deviation of the detected numbers to a mean of the detected numbers forms the characteristic.

12. A method according to claim 1, wherein a shape of a particle is used as a characteristic.

13. A method according to claim 12, wherein the shape of a particle is determined by moments of the particle.

14. A method according to claim 12, wherein the shape of a particle is determined by inverse moments of the particle.

15. -A method according to claim 14, wherein an evaluation of a series of images of the particle in different planes is effected for detecting the inverse moments of the particle, wherein the particle in at least one image lies in focus, and in a number of images in front of the particle or behind the particle, wherein the pixel values of each image are subjected to a Fourier transformation, whereupon DC components are removed or at least reduced, a noise component of the signals is eliminated and a moment evaluation is then effected.

16. A method according to claim 1, wherein an illumination intensity of the light source is held constant and a closed-loop control is provided, which detects the illumination intensity by way of the sensor and actives the light source to accordingly hold the illumination intensity constant.

17. A method according to claim 1, wherein the detection of a particle is effected by way of a comparison of detected characteristics with characteristics of the characteristics collection, by way of a non-linear system comprising a neuronal network.

18. A method according claim 1, wherein the detection of a particle is effected by way of a comparison of detected characteristics with characteristics of the characteristics collection by way of a liner system.

19. A method according to claim 1, wherein an imaging lens is arranged in front of the sensor and the imaging lens has a numeric aperture between 0.05 and 0.4.

20. A method according to claim 1, wherein images of the same fluid sample are evaluated with illumination with a different illumination angle.

21. A method according to claim 1, wherein images of the same fluid sample are evaluated one after another with illumination with light of a different wavelength.

22. A method according to claim 1, wherein a classification of the particle into bacteria, non bacteria or other particles is effected.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In the drawings:

[0028] FIG. 1 is a greatly simplified schematic representation of an optical device for examining a fluid sample in darkfield technology;

[0029] FIG. 2 is a simplified schematic representation of an optical device, according to the state of the art, in brightfield technology;

[0030] FIG. 3 is a view showing a construction of an optical sensor of the optical device;

[0031] FIG. 4 is an image and signal representation of the evaluation of the sensor signal amid the inclusion of the subpixels;

[0032] FIG. 5 is a view of a series of images which shows the same particles in different planes;

[0033] FIG. 6 is a view of eleven consecutive images of the same particle in different planes;

[0034] FIG. 7a is one of three digitalized particle representations of the same particles amid the application of different limit values, with a grey scale representation for comparison;

[0035] FIG. 7b is another of three digitalized particle representations of the same particles amid the application of different limit values, with a grey scale representation for comparison;

[0036] FIG. 7c is another of three digitalized particle representations of the same particles amid the application of different limit values, with a grey scale representation for comparison;

[0037] FIG. 8 is a view of two curves which enclose and determine the areas represented by the particle representations according to FIGS. 7b and 7c;

[0038] FIG. 9 is a view of twenty five images of a particle in consecutive planes, in and out of focus;

[0039] FIG. 10 is a view of the formation of frequency distribution curves on account of the images according to FIG. 9;

[0040] FIG. 11 is a view of the means curve which is formed from the curves according to

[0041] FIG. 10, with deviations for forming a characteristic;

[0042] FIG. 12a is a view of one of three images in the context of the evaluation of the moment of inertia as a characteristic;

[0043] FIG. 12b is a view of another of three images in the context of the evaluation of the moment of inertia as a characteristic;

[0044] FIG. 12c is a view of another of three images in the context of the evaluation of the moment of inertia as a characteristic; and

[0045] FIG. 13 is an amplitude-frequency diagram of a Fourier-transformed image without DC component.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0046] As to how darkfield technology, as is applied with the optical device according to the invention and according to FIG. 1, differs from classic brightfield technology as is represented in FIG. 2, is represented by way of these FIGS. 1 and 2. A light source 1 which beams a fluid sample arranged in a window 2, for illustration only with one particle 3 located therein, is shown in both figures. The fluid sample located in the window 2 is arranged between the optical light source 1 and a CCD sensor 4, in front of which a focusing lens 5 is arranged, which here symbolizes imaging optics. The light source 1 in FIG. 1 is aligned by way of an aperture for example, such that direct beams 6 illuminate the window 2 with the particle 3, but neither directly hit the lens 5 nor the CCD sensor 4 lying therebehind. Only scattered light 7 gets from the particle 3 to the focusing lens 5 and the CCD sensor 4 lying therebehind. An image 8, with which the particle 3 is represented in a white manner against a dark background results from this. The Picture 9 which is represented next to this illustrates how an image of two particles can practically look given this illumination.

[0047] In comparison to this, the light source 1 is directed directly onto the window 2 and the particle 3 located in the fluid as well as the lens 5 and the sensor 4, in FIG. 2. The particle 3 which shadows the direct light 6 is produced by way of the lens 5 as a black dot in the Picture 8, thus on the CCD sensor 4. In the corresponding Picture 9 arranged next to this, one can see three particles whose black edges are distinguished against a grey background.

[0048] In particular small particles can be represented significantly better in the 2D Picture 8, 9 with the darkfield illumination as is represented in FIG. 1 by way of example, than is possible with brightfield illumination as is represented by way of FIG. 2. There, the edge of the particle 3 in Picture 8 is greatly glared. The contrast between the represented particle and the background is extremely high and the resolution within the imaged particle is thus poor, since the direct beams 6 hit the CCD sensor 4. One can clearly recognized with the Picture 9 of FIG. 1 as to which nuanced graduations are already visible with the naked eye against the black background. The darkfield illumination as is represented in FIG. 1 is only to be understood by way of example. Thus, for example, separate light sources can be provided instead of a central light source 1 and these obliquely shine through the window similarly as is shown in FIG. 1. What is essential however is the fact that only scattered light gets from the particle 3 to the lens 5 and the sensor 4 lying therebehind, and no direct radiation gets from the light source to the sensor.

[0049] FIG. 3 shows the structure of a CCD sensor 4 as is commercially widespread today. Each pixel 10 of the sensor 4 consists of four subpixels 11 and specifically of a blue subpixel 11a, a red subpixel 11b and two green subpixels 11c and 11d. The colors, apart from providing each pixel 10 with brightness information, also serve for providing them the color information which is necessary in order to produce a color image.

[0050] With the method according to the invention, 2-D images are produced in black and white, i.e. with grey scales and the color information is not necessary. The subpixels 11 are therefore applied for increasing the resolution and dynamics.

[0051] With regard to the CCD sensor 4 represented in FIG. 3, it is the case of a Bayer sensor with a Bayer matrix. Instead of evaluating the subpixels for retaining the color information as is common, these here on the one hand are used for increasing the resolution, as the respective left upper image of FIG. 4 shows. Moreover, the subpixels 11 or their signals are differently processed. Thus, the subpixels 11a and 11b are low gained, whereas the subpixels 11c and 11d are high gained. A higher dynamics region results by way of this, which is likewise evident by way of FIG. 4 in the left, lower image. As is shown by the left upper image which is produced according to the method described above, the image produced with the above-described method not only has a higher resolution but also higher dynamics, as in particular the grey scales recognizable there illustrate. The resolution is increased by a factor of four and the dynamics are likewise considerably increased. The increased resolution is achieved by way of the use of the subpixels 11 as pixels and the higher dynamics due to the fact that the subpixels each in pairs are low gained or high gained, as is evident in the left upper picture as well as in the signal curve which is located below this. The image which is shown on the right in FIG. 4 and which has a greater resolution and higher dynamics than an image created with a conventional read-out of the CCD sensor then arises by way of interpolating the low gained subpixels with the high-gained subpixels. Even if this improved image information cannot be recognized without further ado on the image or signal representation at the right in FIG. 4, one should take into account the fact that the grey scales which are visible there have a different quality than if these were to be effected with the common evaluation of the CCD sensor signal.

[0052] In order to determine or at least classify a particle detected with the CCD sensor 4, it is necessary to determine various characteristics of the particle and then to compare these with an existing characteristics collection, in order to ascertain as to which types of particles or in the ideal case as to which particle it is a case of. Thereby, the characteristics collection or libraries can be created individually, for example for drinking water, service water, waste water, water from sewage plants or also for other fluids with particles located therein. Thereby, the characteristics collection is to be directed to the demands of the user. Thus, for example, it is essential for drinking water analysis to recognize whether it is the case of bacteria or non bacteria, and moreover of which bacteria. The aim can be to determine or detect E-coli bacteria for example, in order to monitor the water quality. Such characteristics collections as the case may be can be created on location in a self-learning manner if particles which with regard to their shape and their number have been ascertained as being allowable and acceptable, are determined in a number of prior examinations. Thereby, deviations from the previously detected quality can be ascertained with such an automatically created characteristics collection. It is to be understood that a characteristics collection for monitoring drinking water for example can also vary depending on location, thus can be different at different locations, since the bacteria types which could contaminate the water vary depending on the location and the climate. The more characteristics of a particles detected by image correspond to a characteristic in the characteristics collection, the more accurate is the detection. Just a few can be used for the comparison or also a few hundred or even more. The evaluation is effected for example via a neuronal network but can also be effected by way of a linear equation system. However, it has been found that with regard to the question as to whether it is a case of bacteria or non bacteria, a comparison of at least three characteristics, advantageously at least four characteristics is sufficient, in order to determine this with a sufficient reliability. The number of characteristics can be significantly high in special cases.

[0053] The detection of a fluid sample with the quasi stationary particles which are located therein is effected by way of a multitude of images of parallel planes. Thus, for each particle detected by imaging, groups of images of the same particle which represent this particle in different planes are created, with the image evaluation after a first object examination, which e.g. excludes oversized objects such as air bubbles and likewise as well as clearly non-evaluatable regions from the further evaluation (integrity examination). Since a contour-focused representation is only possible in the focus plane due to the aperture of the imaging lens 5 of the optical device, these particles in the groups of images typically appear in several (blurred) pre-focus positions, in one or more focus positions and in several (blurred) post-focus positions. Such images of a particle are represented for example in FIG. 5 which shows 39 images of the same particle in focus and out of focus. With regard to these images one empirically detects as to in which image or images the particle 3 is represented in focus. This image with the particle 3 in focus is indicated at 12 in FIG. 5. The computation is effected in a manner known per se by way of a Sobel operator, thus a simple edge-detection filter which determines this.

[0054] The evaluation of a characteristic of a particle is basically effected in the focused image, unless the characteristic indeed is directed to a property between the focused and non-focused image. The evaluation however, as trials have found, becomes significantly more stable if not only the image 12 in focus, but in each case also an image out of focus in the pre-focus region 13 as well as the post-focus region 14 is evaluated, wherein the application of a neuronal network is then useful, in order to also assign these “blurred” characteristics (FIG. 6).

[0055] A significant characteristic of a particle is always its area in focus, and thereby the size of the area is a characteristic, and the shape of the area another characteristic. A differentiation of the particle in the image is to be fixed in order to detect these variables. This is typically effected by way of fixing a limit value, thus a grey scale of the image which corresponds to a certain pixel value. A pure black-and-white image is produced by way of this limit value, i.e. all pixels whose pixel values are the same or smaller than this limit value are represented in black and all others in white. As to how a change of this limit value affects the image is represented by way of the FIGS. 7a, 7b and 7c. Only the white areas are to be considered in the images according to FIG. 7, and the grey, shadowy areas here only serve for the illustration of the different limit values and would normally be black. The grey shadowy areas show the actual shape of the particle. It is clearly visible in FIG. 7a that the limit value which there has been set at 47% of the maximal pixel value is comparatively high since a large part of the particle falls into the black region and here presumably essential characteristics which are a characteristic of the area are lost. The limit value is set to 30% in FIG. 7b. A significantly different shape of the particle already results here in comparison to the representation according to FIG. 7a. The image according to FIG. 7c, in which the significantly elongate shape of the particles becomes visible and which is simultaneously a significant characteristic of this particle results however if the limit value is reduced to 20%. The method according to the invention now envisages varying the limit value and determining as to whether, in particular the shape of the particle significantly changes at different limit values, in order to then use the limit value which supports this pronounced shape.

[0056] The area of the particle in the image is determined by a hyperbolic curve which is applied in sections around the white area and which defines the area as is shown in FIG. 8, after the black-and-white image has been produced by way of variance of the limit value, said black-and-white image serving as the basis for the further evaluation. The enclosed area, thus the two-dimensional size of the particle can be determined by way of integrating this curve. The respective curve is characterized at 15. For comparison, a curve 16 which results when the image according to FIG. 7b is used as a basis is shown in FIG. 8. It is clear that indeed the variance of the limit value leads to the creation of a characteristic feature with regard to the shape.

[0057] When considering an image stack, it is often to be observed that the area and shape of the detected particle changes within and outside of the focus, as is visible with the images according to FIGS. 5 and 6. Whereas with FIG. 5 it is essentially the position which changes and the other changes are rather due to focus, with the particle detected by way of the images in FIG. 6 it is evidently also the shape. This change is caused by a movement of the particle, i.e. a movement of the particle from one image to the next. If a particle which is not spherical rotates, then the light is influenced depending on how quickly and about which axis it rotates. This is perceived as a flashing with observation by eye. This is a characteristic which is often to be observed with living bacteria, but not with dead bacteria or rarely with particles which are not bacteria. This feature is an important characteristic for determining as to whether it is the case of a bacterium or another particle. In order to determine this characteristic, a stack of images, as is represented for example in FIG. 9, is firstly used for evaluation. The present 25 images which show the particle which is to be determined here, in and out of focus, are evaluated as follows:

[0058] A focus curve is firstly created after the particle to be determined and the images which are considered for this have been selected, as is represented in FIG. 9. The number of images are given by the horizontal axis and the brightness of the four to seven brightest pixels determined in each image, i.e. the pixels which have the greatest pixel value are given by the vertical axis, and specifically in each case forming the mean of these four to seven pixels, so that the curve 16 evident from FIG. 10 results and this has clearly visible peaks. This curve 16 is filtered and smoothed. This smoothed curve is characterized at 17 in FIG. 10. Finally, the mean and the standard deviation are determined for the curve 17. This standard deviation is a characteristic for the detected particle. The greater this value, the larger is this “flash-effect” of the detected particle, which indicates a movement of the particle, in particular a rotation about its axis. These curves 16 and 17 are subtracted from one another in FIG. 10, so that the curve represented in FIG. 11 results, which represents the mean and the deviations from this.

[0059] A further characteristic for determining or classifying particles is the moment formation, which with image processing, in particular evaluation and classification, is counted as belonging to the state of the art. This is regularly effected by way of black-and-white images, preferably after their digitalization into black and white values, as has been described already beforehand.

[0060] A further characteristics is the moment of inertia (also called inverse moment) of a particle, thus also a characteristic which detects the spatial extension of the particle. The starting point of this characteristic for example are seven images, of which one is in focus and three are arranged on each side of the focus, as is given in FIG. 6 by the middle image 12, as well as a pre-focus image 13 and the two images lying between the images 12 and 13 as well as a post-focus image 14 and the two images lying between the focus image and the post-focus image.

[0061] A Fourier transformation is carried out on each of these seven images, of which one is represented by way of example in FIG. 12a, so that an image as is represented in FIG. 12b results. The Fourier transformation in each case results in the particle imaged in the respective image, in reciprocal space (analogously to the reciprocal lattice of crystallography). The direct current components (DC components) are then removed or at least reduced, so that an image as is represented in FIG. 12c results. The noise is also eliminated by way of a suitable filter, whereupon the central moment of each of the objects of the seven images is computed. Such a curve of an image is represented in FIG. 13, wherein the horizontal axis represents the frequency and the vertical axis the amplitude. The DC component or share with the diagram represented in FIG. 13 is already removed, and the noise components 19 are still present there. The maximal and minimal value of this central moment in each case forms a characteristic of the examined particle. The above-described characteristics as a rule are sufficient, in order to determine whether, with regard to the examined particle, it is a case of bacteria or non bacteria.

[0062] A further variance of characteristic can be achieved by way of the rows of images in different planes of the same fluid sample being evaluated with the illumination from different illumination angles and/or with illumination with light of a different wavelength. As to which characteristics are particularly suitable for which particles, in particular bacteria, in order to classify or even determine these necessitates empirical evaluation. The above-described characteristics however are particularly suitable, in order to differentiate bacteria from non bacteria, and specifically as a rule three or four of these characteristics are sufficient, in order to succeed in this classification with an adequately high reliability. Such a classification however is particularly significant with the examination of drinking water, since the detailed determining which is to say a sub-classification and specifically whether the remaining parties are bacteria and not, can be effected after the classification has been effected.

[0063] While specific embodiments of the invention have been shown and described in detail to illustrate the application of the principles of the invention, it will be understood that the invention may be embodied otherwise without departing from such principles.