Label-free automatic acquisition method of cell rotation velocity based on area change algorithm

Abstract

The present invention relates to extracting the rotation speed of cells in an ODEP field based on an area change algorithm. The method comprises: binarizing an RGB image acquired by a CCD camera to extract cell morphological features; identifying and selecting cells by calculating connected domains in the binary image; calculating the area of ​​the selected cells using morphological methods; calculating the area of ​​all frames of the selected cells to obtain a cell area change curve; smoothing the change curve using convolution calculations to remove noise from the curve; and obtaining the cell rotation speed using the periodic changes shown on the processed cell change curve according to rotation theory using the values ​​of every two adjacent peaks and valleys. The method can accurately and automatically obtain the rotation speed of a cell of interest from a video containing various motions and cells; extract the complete morphology of the cell; and quickly obtain the motion trajectory of the cell of interest, thereby performing a comprehensive analysis of the cell. The method has applications in biomedicine, bioengineering, and other related fields.

Description

Technical Field

[0001] The present invention relates to biomedicine, bioengineering and other related fields, and in particular to a label-free automatic acquisition method for cell rotation speed based on an area change algorithm. Background Art

[0002] As a form of cellular behavior, cell rotation characteristics are of great significance for exploring and elucidating the intrinsic characteristics of cells, such as electrophysiology, biomechanics, and dielectric properties. These intrinsic characteristics are related to human diseases. Therefore, studying cell rotation characteristics can determine the cell rotation speed and improve our understanding of the effectiveness of cancer treatment. However, there is currently a lack of efficient means to extract cell rotation speed, requiring manual counting, which can only extract the cell rotation speed with low precision. To achieve improved accuracy in determining cell rotation and automate the entire calculation process, it is urgent to develop a fast real-time detection algorithm based on machine vision to achieve the acquisition of cell behavior characteristics. Currently, a method that can accurately extract cell rotation speed in real time in the ODEP field based on the area change algorithm has not been reported. Summary of the Invention

[0003] To address the above issues, the present invention provides a method for extracting cell rotation speed in an ODEP field based on an area change algorithm, enabling calculation of cell rotation speed under the ODEP field. This invention addresses the application of ODEP field microfluidic chip technology in life sciences. Specifically, it provides a method for extracting cell rotation speed in an optically-induced dielectrophoresis (ODEP) field based on machine vision. This method can be applied to the real-time identification, classification, calibration, and multidimensional information acquisition of various cancer cells.

[0004] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0005] The method for automatically acquiring cell rotation speed without labeling based on an area change algorithm includes the following steps:

[0006] 1) Obtaining an RGB image containing cells in a light-induced dielectrophoresis field and preprocessing it to obtain a binary image;

[0007] 2) Calculate the connected domain of the binary image to identify cells in the image, mark the cells of interest and calculate the area of ​​the cells;

[0008] 3) Repeat steps 1) to 2) until the areas of the cells of interest in all RGB images are calculated and the area change curve is drawn;

[0009] 4) Use convolution to remove curve noise;

[0010] 5) The cell rotation speed is obtained by calculating the processed curve.

[0011] The preprocessing of RGB images includes the following steps:

[0012] Perform shadow correction on RGB images;

[0013] Perform contrast enhancement on the corrected image;

[0014] The enhanced image is binarized using the Otsu method to obtain a binary image.

[0015] Perform shading correction on RGB images using a log-entropy based method.

[0016] By performing grayscale transformation on the image, the grayscale value of the image is stretched to the set range to achieve image contrast enhancement.

[0017] The cell rotation speed was calculated by the following formula:

[0018]

[0019] Among them, f fps Indicates the frame rate, X i and X i-1 represents the frame number of the adjacent trough, and n is the average rotation speed.

[0020] The label-free automatic acquisition system of cell rotation speed based on area change algorithm includes:

[0021] A preprocessing module is used to obtain an RGB image containing cells in a light-induced dielectrophoresis field and preprocess the image to obtain a binary image;

[0022] The target recognition calculation module is used to calculate the connected domain of the binary image to identify the cells in the image, mark the cells of interest, calculate the area of ​​the cells, and draw the area change curve;

[0023] Convolution module, used to remove curve noise using convolution;

[0024] The speed calculation module is used to obtain the rotation speed of the cell by calculating the processed curve.

[0025] The preprocessing module includes:

[0026] Shadow correction module, used to perform shadow correction on RGB images;

[0027] A contrast enhancement module, used for performing contrast enhancement on the corrected image;

[0028] The binarization module uses the Otsu method to binarize the enhanced image to obtain a binary image.

[0029] A label-free automatic acquisition system for cell rotation speed based on an area change algorithm comprises a memory and a processor; the memory is used to store a computer program; the processor is used to implement the label-free automatic acquisition method for cell rotation speed based on an area change algorithm when executing the computer program.

[0030] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the method for automatically acquiring cell rotation speed without labeling based on an area change algorithm is implemented.

[0031] The present invention has the following beneficial effects and advantages:

[0032] 1. In addition to manually selecting cells of interest during cell selection, the remaining steps of the present invention can achieve automatic cell tracking.

[0033] 2. The present invention has a good processing effect on cell microscopic images with poor experimental lighting environment and low contrast, and can clearly extract cell contours.

[0034] 3. The present invention does not require repeated observation of videos and manual counting, which reduces human interference in experimental results and achieves greater stability.

[0035] 4. The present invention uses a convolution algorithm to process the curve, which can accurately determine the period of cell rotation and improve the accuracy of obtaining the cell's rotation speed.

[0036] 5. The present invention has the advantages of automation, high accuracy and good reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of the cell rotation speed acquisition system flow of the present invention.

[0038] Figure 2 Schematic diagram of removing dark corners based on entropy.

[0039] Figure 3 Schematic diagram of contrast enhancement.

[0040] Figure 4 Schematic diagram of image binarization using Otsu's method.

[0041] Figure 5 Schematic diagram of labeling and selecting cells.

[0042] Figure 6 To obtain the original cell area change curve.

[0043] Figure 7 This is the curve of cell area change after convolution processing. DETAILED DESCRIPTION

[0044] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0045] The method for extracting the cell rotation speed in the ODEP field based on the area change algorithm includes the following steps:

[0046] Step 1: Perform shading correction based on logarithmic entropy on the RGB image captured by the CCD (charge-coupled-device) camera;

[0047] Step 2: Enhance the contrast of the image;

[0048] Step 3: Perform Otsu's method to binarize the image.

[0049] Step 4: Perform cell identification and select cells of interest, and calculate the area of ​​the selected cells; perform the above processing on all frames to obtain the cell area change curve.

[0050] like Figure 1 As shown, the present invention takes Raji cells (a subtype of lymphoma cell) as an example, utilizes an area change algorithm to accurately and automatically obtain the cell rotation speed, and preprocesses the RGB (red-green-blue) image acquired by a CCD (charge-coupled-device) camera to remove dark corners, enhance contrast, and binarize it into a binary image. A morphological method is used to calculate the connected domain, identify and mark the cells of interest, calculate the cell area, and batch process all images to obtain an area change curve. Convolution is used to remove curve noise, and the cell rotation speed is calculated using every two adjacent peak and valley points.

[0051] like Figure 2 As shown in FIG, based on the entropy operation, the distribution of low brightness in the dark corner is made closer to the normal brightness in the middle of the image, thereby achieving the effect of removing the dark corner.

[0052] like Figure 3 As shown in the figure, the grayscale values ​​of the entire image pixels are mostly small, which makes the entire image very dim and the contrast is not high. By grayscale transformation, the grayscale value is stretched to the specified range, and its contrast is obviously greatly enhanced. Using formula (1)

[0053]

[0054] Among them I min , I maxare the minimum and maximum grayscale values ​​of the original image, and MIN and MAX are the minimum and maximum grayscale values ​​of the grayscale space to be stretched.

[0055] like Figure 4 As shown, the threshold level is determined using the Otsu method (a classic threshold selection method that selects a threshold to maximize the between-class variance) and the image is converted into binary form.

[0056] like Figure 5 As shown, in the binary image of the segmentation result, the two cells in the image are marked by calculating the connected domain; the cell to be measured is selected and extracted separately; and the area of ​​the cell is calculated.

[0057] like Figure 6 As shown in the figure, the horizontal axis is the cell area, and the vertical axis is the number of image frames. The experimental image has 159 frames. The above operation is performed on each frame to obtain the area change curve of this cell projected on the two-dimensional plane when it rotates in the physical field. The area curve shows that the cell area changes periodically, that is, the rotation speed can be calculated based on the period of area change.

[0058] like Figure 7 As shown in the figure, the horizontal axis is the cell area and the vertical axis is the number of image frames. To obtain a clearer cycle, the area curve is de-noised using convolution to filter out excess peaks. The rotation speed is calculated using the processed area change curve. When the cell area is at the trough within the area change cycle, that is, the area is at its minimum value, when the area changes to the trough of the next cycle, we can determine that the cell has rotated one circle. The final rotation speed is calculated using formula (2).

[0059]

[0060] where f fps Indicates the frame rate of the video, f fps =15,X i and X i-1 Indicates the frame number of the adjacent maximum point, and n is the average rotation speed (in rpm). Figure 7 Shows the 10V pp The rotation speed of Raji cells at a constant AC bias voltage of 70kHz is 86 rpm.

Claims

1. A label-free automatic acquisition method for cell rotation speed based on area change algorithm, characterized in that: The following steps are involved: 1) Obtaining an RGB image containing cells in a light-induced dielectrophoresis field and preprocessing it to obtain a binary image; 2) Calculate the connected domain of the binary image to identify cells in the image, mark the cells of interest and calculate the area of ​​the cells; 3) Repeat steps 1) to 2) until the areas of the cells of interest in all RGB images are calculated and the area change curve is drawn; 4) Use convolution to remove curve noise; 5) Obtain the cell rotation speed by calculating the processed curve; The cell rotation speed was calculated by the following formula: Among them, f fps Indicates the frame rate, X i and X i-1 represents the frame number of the adjacent trough, and n is the average rotation speed.

2. The label-free automatic acquisition method of cell rotation speed based on area change algorithm according to claim 1, characterized in that: The preprocessing of RGB images includes the following steps: Perform shadow correction on RGB images; Perform contrast enhancement on the corrected image; The enhanced image is binarized using the Otsu method to obtain a binary image.

3. The label-free automatic acquisition method of cell rotation speed based on area change algorithm according to claim 2, characterized in that: Perform shading correction on RGB images using a log-entropy based method.

4. The label-free automatic acquisition method of cell rotation speed based on area change algorithm according to claim 2, characterized in that: By performing grayscale transformation on the image, the grayscale value of the image is stretched to the set range to achieve image contrast enhancement.

5. A label-free automatic acquisition system for cell rotation speed based on area change algorithm, characterized by: include: A preprocessing module is used to obtain an RGB image containing cells in a light-induced dielectrophoresis field and preprocess the image to obtain a binary image; The target recognition calculation module is used to calculate the connected domain of the binary image to identify the cells in the image, mark the cells of interest, calculate the area of ​​the cells, and draw the area change curve; Convolution module, used to remove curve noise using convolution; A speed calculation module is used to obtain the rotation speed of the cell by calculating the processed curve; The cell rotation speed was calculated by the following formula: Among them, f fps Indicates the frame rate, X i and X i-1 represents the frame number of the adjacent trough, and n is the average rotation speed.

6. The label-free automatic acquisition system for cell rotation speed based on area change algorithm according to claim 5, characterized in that: The preprocessing module includes: Shadow correction module, used to perform shadow correction on RGB images; A contrast enhancement module, used for performing contrast enhancement on the corrected image; The binarization module uses the Otsu method to binarize the enhanced image to obtain a binary image.

7. A label-free automatic acquisition system for cell rotation speed based on area change algorithm, characterized by: It comprises a memory and a processor; the memory is used to store a computer program; the processor is used to implement the label-free automatic acquisition method of cell rotation speed based on the area change algorithm as described in any one of claims 1 to 4 when executing the computer program.

8. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, the label-free automatic acquisition method of cell rotation speed based on the area change algorithm according to any one of claims 1 to 4 is implemented.

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