A high-definition imaging method for rapid scanning microscopy of phytoplankton for counting frame counting

Abstract

The present invention discloses a phytoplankton fast scanning microscopic high-definition imaging method for counting frame counting, which belongs to the field of resources and environment. To address the problems of slow scanning speed and low focusing sensitivity encountered in the process of obtaining clear microscopic images of phytoplankton counting frame samples, the method utilizes the characteristics that phytoplankton has obvious color characteristics and the color characteristics of the target area become more obvious in the process of microscopic field of view from defocus to focus. The method evaluates the focus of the phytoplankton microscopic image by fusing the grayscale texture characteristics and color saturation of the microscopic image to improve the sensitivity of autofocus. Then, the discrete focus distances of different microscopic fields of view of the counting frame sample are randomly obtained, and bilinear interpolation is used to perform counting frame focus surface interpolation, thereby realizing the rapid acquisition of clear phytoplankton microscopic images.

Description

Technical Field

[0001] The invention belongs to the field of resources and environment, and in particular relates to a phytoplankton rapid scanning microscopic high-definition imaging method for counting frame counting. Background Art

[0002] Phytoplankton is an important part of water quality biological assessment. The national ecological environment standard "Determination of Phytoplankton in Water Quality 0.1ml Counting Frame-Microscope Counting Method" (HJ1216-2021) stipulates the determination method of phytoplankton in water. Professional microscopy personnel need to drop the water sample into the counting frame (taking the 0.1mL standard counting frame as an example, a total of 10 10 = 100 counting squares. The number of microscopic fields in each square is related to the magnification and camera. The slide microscopic field is moved to the corresponding counting square according to the diagonal method, row method, whole slide method, or random field method, and the number of phytoplankton species in each microscopic field is recorded. The operation process is cumbersome.

[0003] In recent years, deep learning-based image recognition technology has rapidly developed for the automated identification of phytoplankton. Combined with microscopic imaging optical path structures, hardware, and host computer operating procedures, it can replace traditional manual microscopic inspection. High recognition accuracy relies on clear, high-magnification microscopic images of phytoplankton. However, the depth of field of high-magnification microscopes is as small as micrometers, requiring the same focus distance for all counting frame scanning fields. Therefore, as a computer program drives the stepper motor to move the slide to acquire microscopic images of different fields of view, the phytoplankton microscopic field of view must be focused multiple times using autofocus technology, resulting in longer scanning times for the counting frame samples. At the same time, autofocus technology uses motor control to obtain microscopic images of different focal lengths. After determining the target area through the focus window selection method, the image focus evaluation method is used to calculate the clarity of the target area in microscopic images of different focal lengths to obtain a clear microscopic image. However, the position of phytoplankton in the microscopic image is random, and the focus window is easy to deviate from the target area. When the focus evaluation is performed by calculating the clarity of the entire microscopic image, the calculation result is seriously interfered by the background area, and it is difficult to accurately measure the clarity of the phytoplankton target area, resulting in low sensitivity of the focus evaluation curve and weak ability to distinguish the degree of defocus. Summary of the Invention

[0004] To address the problems of slow scanning speed and low focus sensitivity encountered in the process of acquiring clear microscopic images of phytoplankton counting frame samples, this paper takes advantage of the fact that the color characteristics of phytoplankton are obvious and the color characteristics of the target area become more and more obvious in the process of microscopic field of view from defocus to focus. By fusing the grayscale texture characteristics and color saturation of the microscopic image, the focus of the phytoplankton microscopic image is evaluated to improve the autofocus sensitivity. Then, the discrete focus distances of different microscopic fields of view of the counting frame sample are randomly obtained, and the focus distance interpolation operation is performed through bilinear interpolation, so as to achieve the rapid acquisition of clear phytoplankton microscopic images.

[0005] The technical solution of the present invention is as follows:

[0006] A high-definition microscopic imaging method for rapid scanning of phytoplankton for counting frame counting includes the following steps:

[0007] Step 1. Sample Scan

[0008] Scan the counting frame sample by diagonal method, row grid method, whole slide method or random field method;

[0009] Step 2: Scan path optimization

[0010] The position coordinates of the scanning field of view are used as vertices and the connections between fields of view are used as edges between vertices to construct an undirected complete graph of the scanning field of view. :

[0011] ,

[0012] in, is the set of scan field coordinates, For the edges connecting different views, in the constructed undirected complete graph All coordinates in are connected;

[0013] The greedy algorithm is used to determine the optimal scanning path, and the distance between any two fields of view is defined as the sum of the X-axis distance and the Y-axis distance:

[0014] ,

[0015] Calculate the distance between all unscanned field of view coordinates and the current field of view coordinates, determine the coordinate with the shortest distance as the next scanning position, and iteratively calculate the optimal scanning path of the selected scanning method;

[0016] Step 3: Select discrete focus points

[0017] Randomly select from all microscopic fields A discrete focus point that needs to be automatically focused is set, and then the counting frame is moved to the microscope field of view to be focused;

[0018] Step 4: Extract grayscale texture features

[0019] Phytoplankton microscopic grayscale image by bilateral filtering To perform noise reduction:

[0020] ,

[0021] in, Center point The adjacent pixels of is a microscopic grayscale image of phytoplankton. If the adjacent point is , the standard deviation of the Gaussian kernel in the spatial domain is , the standard deviation of the Gaussian kernel on the pixel value domain is ,but is the weight function;

[0022] ,

[0023] Extraction using Laplace operator The grayscale characteristics of phytoplankton are obtained by edge information :

[0024] ,

[0025] Step 5: Extract color features

[0026] The phytoplankton microscopic image format is converted from RGB to HSV color space, and the color saturation S component feature is extracted as the color feature information of phytoplankton. The color saturation difference of phytoplankton microscopic images is characterized by variance:

[0027] ,

[0028] ,

[0029] in, for point The saturation value at is the average saturation value, for point The difference between the saturation at the pixel and the average saturation of the entire image;

[0030] Step 6: Focus on evaluation

[0031] Focus evaluation is performed by integrating the grayscale gradient features and color features of phytoplankton microscopic images:

[0032] ,

[0033] Where, The midpoint of the phytoplankton microscopic image The grayscale features at The midpoint of the phytoplankton microscopic image The color characteristics of The midpoint of the phytoplankton microscopic image The saturation value at ;

[0034] Step 7: Switch the microscope field of view

[0035] Move the counting frame sample to the next microscopic field of view and follow steps 4, 5 and 6 in sequence, recording the horizontal position coordinates and vertical focus distance of each microscopic field of view. , thereby obtaining the counting frame sample focus surface discrete values;

[0036] Step 8: Focus Surface Interpolation

[0037] Let the target point The minimum rectangle distances to the four vertices are , , , . First, perform linear interpolation in the horizontal X direction:

[0038] ,

[0039] ,

[0040] Then perform linear interpolation in the Y direction to get the focus distance of the target point:

[0041] .

[0042] Beneficial effects:

[0043] The present invention provides a fast scanning microscopic high-definition imaging method for phytoplankton counting by means of a counting frame. The method first determines the shortest scanning path for all fields of view, then calculates the focusing distances of all microscopic fields of view of the phytoplankton counting frame sample, and drives the motor counting frame to move to the focusing distance of the corresponding microscopic field of view through a computer program. This avoids the need to perform an automatic focusing operation on each microscopic field of view, significantly reduces the scanning time, and thus enables the rapid acquisition of clear microscopic images of the phytoplankton counting frame sample. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Flowchart of the method for rapid scanning of clear microscopic images of frame samples for phytoplankton counting.

[0045] Figure 2 This is an example diagram of the optimal route scanned by the random field of view method.

[0046] Figure 3This is an example diagram for comparing normalized focusing evaluation curves.

[0047] Figure 4 This is an example diagram of the average offset distance.

[0048] Figure 5 This is an example diagram of focus offset distance.

[0049] Figure 6 An example of a row grid scan of a sample frame for phytoplankton counting. DETAILED DESCRIPTION

[0050] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. However, the following embodiments are intended only to explain the present invention, and the scope of protection of the present invention should include the entire contents of the claims. Moreover, through the description of the following embodiments, those skilled in the art can fully implement the entire contents of the claims of the present invention.

[0051] Example

[0052] like Figure 1 As shown in FIG, a fast scanning microscopic high-definition imaging method for phytoplankton counting by counting frame is provided, and the steps are as follows:

[0053] (1) Select the scanning method

[0054] According to the national environmental standard HJ1216-2021, the counting frame sample can be scanned by diagonal method, row grid method, full slide method or random field of view method. 10 = 100 counting squares, with the number of microscopic fields per square being 2 Taking 4=8 as an example (depending on the imaging optical path, camera configuration, etc.), the diagonal method scans 10 counting squares along the diagonal, totaling 80 microscopic fields; the row method scans rows 2, 5, and 8, totaling 240 microscopic fields; the full-slide method scans 100 counting squares, totaling 800 microscopic fields; and the random field method randomly selects a specified number of microscopic fields. Users can select the scanning method through the software interface.

[0055] (2) Scan path optimization

[0056] In order to quickly complete the scanning of the counting frame sample, the present invention uses the position coordinates of the scanning field of view as vertices and the connections between fields of view as edges between vertices to construct an undirected complete graph of the scanning field of view. As shown in the following formula.

[0057] ,

[0058] In this example, is the set of scan field coordinates, For the edges connecting different views, in the constructed undirected complete graph All coordinates in are connected.

[0059] Then, a greedy algorithm is used to determine the optimal scanning path. Since the motor can only move in the X-axis and Y-axis directions, the present invention defines the distance between any two fields of view as the sum of the X-axis distance and the Y-axis distance, as shown in the following formula.

[0060] ,

[0061] The distances between all unscanned field of view coordinates and the current field of view coordinates are calculated, and the coordinate with the shortest distance is determined as the next scanning position. The optimal scanning path of the selected scanning method is calculated iteratively in sequence.

[0062] (3) Select discrete focus points

[0063] Randomly select from all microscopic fields The computer program then drives the motor to move the counting frame to the microscopic field of view to be focused.

[0064] (4) Extracting grayscale texture features

[0065] Compared with the out-of-focus image, the edge details of the focused image are clearer. Existing research results show that in scenes with less noise, the Laplacian operator can evaluate the focus of the image with high quality. Bilateral filtering achieves image noise reduction while maintaining the image edge details by nonlinearly combining the image grayscale information and spatial information. As shown in the following formula, first, bilateral filtering is used to filter the phytoplankton microscopic grayscale image. Perform noise reduction to reduce the impact of noise on focus evaluation.

[0066] ,

[0067] in, Center point The bilateral filter constructs a weight function by using the Euclidean distance and grayscale value difference between pixels. , which can achieve image noise reduction while preserving image edges.

[0068] ,

[0069] Phytoplankton microscopic grayscale image After bilateral filtering and denoising, the Laplace operator is used to extract The grayscale characteristics of phytoplankton can be obtained by using the edge information of :

[0070] ,

[0071] (5) Extracting color features

[0072] Phytoplankton microscopic images usually consist of a background area and a phytoplankton cell target area, and the target area has obvious color features, such as blue algae appearing blue and green algae appearing green. In the focused state, the color saturation of phytoplankton is relatively bright, and as the microscope defocuses to varying degrees, its color saturation information gradually weakens. Therefore, the focus of the microscopic image can be evaluated by extracting the color feature information of phytoplankton. The experiment converted the phytoplankton microscopic image format from RGB to HSV color space, and extracted the color saturation S component feature as the color feature information of phytoplankton. For the background area, since the color feature is not obvious, its color saturation value is always low; for the phytoplankton target area, when in the defocused state, the color information of the microscopic image is weak and the color saturation value is relatively low; when in the focused state, the microscopic image color is bright and the color saturation value is relatively high. Therefore, compared with the defocused blurred image, the phytoplankton microscopic focused image has a greater saturation difference. This embodiment characterizes the color saturation difference of the phytoplankton microscopic image by variance:

[0073] ,

[0074] ,

[0075] in, for point The saturation value at is the average saturation value, for point The difference between the saturation at that location and the average saturation of the entire image.

[0076] (6) Focus on evaluation methods

[0077] Background areas in phytoplankton microscopic images can easily interfere with focus evaluation results. When the microscope is in focus, the saturation of the background area is slightly different from the average saturation of the entire image, while the saturation of the phytoplankton target area is significantly different from the average saturation of the entire image. Based on this, the present invention proposes integrating the grayscale gradient characteristics and color features of phytoplankton microscopic images for focus evaluation to improve the resolution of different degrees of defocus. This is shown in the following formula.

[0078] ,

[0079] In the formula 、 、 are the midpoints of the phytoplankton microscopic images. The grayscale features, color features and saturation values ​​at .

[0080] (7) Switching the microscope field of view

[0081] Use the computer program to drive the motor to move the counting frame sample to the next microscopic field of view, and follow the above steps (4-6) in sequence, recording the horizontal position coordinates and vertical focus distance of each microscopic field of view. , thereby obtaining the focusing surface of the counting frame sample discrete values.

[0082] (8) Focus surface interpolation

[0083] Let the target point The minimum rectangle distances to the four vertices are , , , First, perform linear interpolation in the horizontal X direction, as shown below:

[0084] ,

[0085] ,

[0086] Then perform linear interpolation in the Y direction to obtain the focus distance of the target point, as shown in the following formula.

[0087] ,

[0088] Through the above steps, the shortest scanning path of all fields of view is first determined, and then the focusing distances of all microscopic fields of view of the phytoplankton counting frame sample are calculated. The motor counting frame is driven by a computer program to move to the focusing distance of the corresponding microscopic field of view, avoiding the need for automatic focusing operation on each microscopic field of view, greatly reducing the scanning time, and thus realizing the rapid acquisition of clear microscopic images of the phytoplankton counting frame sample.

[0089] The present invention discloses a method for quickly scanning clear microscopic images of phytoplankton counting frame samples, and demonstrates the discovery effect from two aspects: optimal path selection, phytoplankton microscopic image clarity evaluation, and focus surface interpolation.

[0090] In terms of optimal path selection, taking the random horizon method as an example, the coordinates and Euclidean distance information of the undirected complete graph of 10 random horizons are selected as shown in Table 1.

[0091] Table 1 Random field of view position coordinates and distance examples

[0092]

[0093] The scanning path obtained by the greedy algorithm is as follows Figure 2 As shown, there are Figure 2 It can be seen that the optimal path scanning method provided by the present invention can obtain a scanning route with an optimal distance.

[0094] To evaluate clarity, a precision stage was used to capture a microscopic image of phytoplankton every 2 μm of movement in the depth of field. Pediastrum sp., Coelastrum sphaericum, Selenastrum capricornutum, and Phaeodactylum were selected for analysis. Pediastrum and Coelastrum sphaericum are characterized by their relatively large colony-like cells, while Phaeodactylum and Phaeodactylum are smaller, single-celled algae. A sequence of 31 phytoplankton microscopic images was acquired sequentially along the depth of field. Figure 3 Comparison chart of the normalized focusing evaluation curves of the proposed method and Tenengrad, variance method, Brenner, Laplacian and Roberts.

[0095] Traditional focus evaluation methods calculate the gradient information of the entire phytoplankton microscopic image. However, phytoplankton microscopic images have a large background area, and the normalized focus evaluation curve has higher evaluation values ​​in the flat areas. This invention effectively improves the resolution of images with varying degrees of defocus by fusing the edge gradient characteristics and color saturation characteristics of phytoplankton microscopic images.

[0096] In terms of focus surface interpolation, Figure 4 The average offset distance corresponding to each specified number of steps along the horizontal and vertical directions is given by Figure 4 It can be seen that the average offset distance is linearly correlated with the focus interval: the larger the focus interval, the larger the average offset distance. Furthermore, the same focus interval has different effects on the horizontal and vertical focus distances, with the average horizontal offset distance being higher than the vertical one. Therefore, when selecting the focus interval, the vertical focus interval should be larger than the horizontal focus interval.

[0097] Figure 5 With the vertical focus interval set to 3 and the horizontal focus interval set to 2, a sample graph of the focus offset distances for 800 microscopic fields was plotted. At a 20x magnification, the focal length range for clear phytoplankton microscopic images can be approximated to be within 8 microns, indicating a 95.13% probability of accurately obtaining clear microscopic images in all 800 microscopic fields. The experimental results demonstrate that by automatically focusing on discrete sampling points within all microscopic fields and interpolating the focus surface using interpolation techniques, a focus surface can be effectively obtained, significantly reducing the number of autofocus cycles during scanning of the phytoplankton counting frame, thereby achieving rapid scanning of the counting frame sample.

[0098] The method for rapid scanning of clear microscopic images of phytoplankton counting frame samples was integrated into the automatic recognition system for phytoplankton microscopic images. Diagonal scanning, grid scanning, and full-slice scanning were performed on 0.1 mL phytoplankton counting frame samples, achieving rapid acquisition and automatic identification of clear microscopic images of phytoplankton. Figure 6 The following figure shows the sample scanning process of the counting frame using the row grid method as an example. 10 The table shows 100 counting squares in a 0.1 mL standard counting frame, and the value is the number of cells in each counting square; 4 Table 2 shows the 8 microscopic fields within each counting grid, and the values ​​are the number of cells in each microscopic field.

[0099] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

Claims

1. A high-definition microscopic imaging method for rapid scanning of phytoplankton for counting frame counting, characterized in that: The steps include: Step 1: Sample scanning; Step 2: Scan the path to find the best solution; Step 3: Select discrete focus points; Step 4: Extract grayscale texture features; Step 5: Extract color features; Step 6: Focus on evaluation; Step 7: Switch the microscope field of view; Step 8: Focus surface interpolation; In step 2, the position coordinates of the scan field of view are used as vertices and the connections between fields of view are used as edges between vertices to construct an undirected complete graph of the scan field of view. : ， in, is the set of scan field coordinates, For the edges connecting different views, in the constructed undirected complete graph All coordinates in are connected; The greedy algorithm is used to determine the optimal scanning path, and the distance between any two fields of view is defined as the sum of the X-axis distance and the Y-axis distance: ， Calculate the distance between all unscanned field of view coordinates and the current field of view coordinates, determine the coordinate with the shortest distance as the next scanning position, and iteratively calculate the optimal scanning path of the selected scanning method; In step 4, the phytoplankton microscopic grayscale image is filtered by bilateral filtering. To perform noise reduction: ， in, Center point The adjacent pixels of is a microscopic grayscale image of phytoplankton. If the adjacent point is , the standard deviation of the Gaussian kernel in the spatial domain is , the standard deviation of the Gaussian kernel on the pixel value domain is ,but is the weight function; ， Extraction using Laplace operator The grayscale characteristics of phytoplankton are obtained by edge information : ； In step 6, the grayscale gradient features and color features of the phytoplankton microscopic image are fused to perform focus evaluation: ， Where, The midpoint of the phytoplankton microscopic image The grayscale features at The midpoint of the phytoplankton microscopic image The color characteristics of The midpoint of the phytoplankton microscopic image The saturation value at ; In step 7, move the counting frame sample to the next microscopic field of view, and follow steps 4, 5, and 6 in sequence to record the horizontal position coordinates and vertical focus distance of each microscopic field of view. , thereby obtaining the focusing surface of the counting frame sample discrete values; In step 8, let the target point The minimum rectangle distances to the four vertices are , , , , first perform linear interpolation in the horizontal X direction: ， ， Then perform linear interpolation in the Y direction to get the focus distance of the target point: 。 2. The method for rapid scanning microscopic high-definition imaging of phytoplankton for counting frame counting according to claim 1, characterized in that: In step 1, the counting frame sample is scanned using the diagonal method, row grid method, full slide method, or random field of view method.

3. The method for rapid scanning microscopic high-definition imaging of phytoplankton for counting frame counting according to claim 1, characterized in that: In step 3, randomly select Select a discrete focus point that needs to be automatically focused, and then move the counting frame to the microscope field to be focused.

4. The method for rapid scanning microscopic high-definition imaging of phytoplankton for counting frame counting according to claim 1, characterized in that: In step 5, the phytoplankton microscopic image format is converted from RGB to HSV color space, and the color saturation S component feature is extracted as the color feature information of phytoplankton. The color saturation difference of the phytoplankton microscopic image is characterized by variance: ， ， in, for point The saturation value at is the average saturation value, for point The difference between the saturation at that location and the average saturation of the entire image.

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