Image enhancement method and system for high-sensitivity detection of weak signals in immunochromatography

Abstract

This invention discloses an image enhancement method and system for highly sensitive detection of weak signals in immunochromatographic assays. The method includes: projecting a digital image of a fluorescent quantum dot immunochromatographic test strip along the longitudinal direction of the test strip and solving for the illuminance image using a basis vector decomposition function; separating the digital image into three channels (R, G, and B), removing the illuminance image of each channel to obtain the fluorescence signal image of each channel, processing and fusing the fluorescence signal images of the three channels to obtain an enhanced fluorescence intensity image; projecting the enhanced fluorescence intensity image, reading the peak value of the projection curve, obtaining the fluorescence intensity values ​​of the control line (C line) and the detection line (T line) of the test strip, and predicting the sample concentration based on the T line. The advantages of this invention are: high accuracy and sensitivity in detecting the intensity of quantum dot immunochromatographic signals.

Description

Technical Field

[0001] This invention relates to the fields of immunochromatographic detection technology and digital image processing, and more specifically to an image enhancement method for highly sensitive detection of weak signals in immunochromatographic analysis. Background Technology

[0002] The principle of immunochromatography is to pre-immobilize specific antibodies on a specific area of ​​a nitrocellulose membrane. When one end of the dried nitrocellulose membrane is immersed in the sample, the sample moves along the membrane to the other end by capillary action. When it moves to the area where the antibody is immobilized, the corresponding antigen in the sample specifically binds to the antibody. If immunochromatography is used to stain the area with colloidal gold or immunoenzymes, a certain color can be displayed, thereby achieving specific immunodiagnosis.

[0003] The detection sensitivity of existing immunochromatographic techniques based on colloidal gold nanoprobes has always been severely insufficient. In recent years, immunochromatographic techniques that enhance the detection signal intensity by preparing composite quantum dot nanoprobes have developed rapidly. However, at the signal readout end, factors such as light flux loss caused by filters and interference from diffuse reflection light make the detection effect of quantum dot immunochromatography unstable for low-concentration samples, resulting in a large number of missed detections and false positives.

[0004] The invention patent application with publication number CN101493460A discloses a method for preparing fluorescent microsphere immunochromatographic test strips and a quantitative detection method. It provides a method for identifying the concentration of analytes by enhancing the fluorescence signal through photomultiplier tubes. However, when the test strip is prepared based on color, other light will inevitably affect the detection results. This method does not consider how to filter out the influence of other light, so it cannot provide an accurate and highly sensitive detection method. Summary of the Invention

[0005] The technical problem to be solved by this invention is that the accuracy and sensitivity of existing immunochromatographic techniques are not high.

[0006] This invention solves the above-mentioned technical problems through the following technical means: an image enhancement method for highly sensitive detection of weak signals in immunochromatography, the method comprising:

[0007] Image illuminance estimation involves projecting the digital image of the fluorescent quantum dot immunochromatographic test strip along the longitudinal direction of the test strip and solving for the illuminance image using a basis vector decomposition function.

[0008] Signal enhancement involves separating the digital image into three channels: R, G, and B. The illuminance image of each channel is removed to obtain the fluorescence signal image of each channel. The fluorescence signal images of the three channels are then processed and fused to obtain an enhanced fluorescence intensity image.

[0009] Intensity calculation involves projecting the enhanced fluorescence intensity image, reading the peak value of the projection curve, obtaining the fluorescence intensity values ​​of the control line (C line) and the detection line (T line) of the test strip, and predicting the sample concentration based on the T line.

[0010] Beneficial effects: This invention estimates the illuminance of the digital image of the immunochromatographic test strip, quickly decomposes it into an illuminance image, then separates the digital image into three channels (R, G, B), removes the illuminance image of each channel to obtain the fluorescence signal image of each channel, processes and fuses them to obtain an enhanced fluorescence intensity image, and then uses the enhanced fluorescence intensity image to obtain the control line and detection line of the test strip. Because the fluorescence intensity image is enhanced, the intensity of the quantum dot immunochromatographic signal can be detected quickly and accurately even for low-concentration samples, with high sensitivity.

[0011] Furthermore, the image illumination estimation is preceded by image relationship analysis, the process of which is as follows:

[0012] Digital images of fluorescent quantum dot immunochromatographic test strips were acquired, and the relationship between the digital images, illumination images, and fluorescence signal images was obtained.

[0013] F(x,y)=R(x,y)·L(x,y)

[0014] Deformation

[0015]

[0016] Among them, the fluorescence intensity image R(x,y) is the excitation light information of the quantum dot, that is, the true attribute of fluorescence intensity, and the illuminance image L(x,y) is the diffuse reflection light information generated by the reflected light of fluorescence and the incident blue-green light, which is the dynamic interference information of the image.

[0017] Furthermore, the process of estimating the image illumination is as follows:

[0018] S21. The light intensity in the test strip image is linearly distributed along the longitudinal direction of the nitrocellulose membrane. Therefore, projecting the image longitudinally yields the brightness distribution of the image. The projection transformation formula is:

[0019]

[0020] In the formula, ProJ(y) is the projection vector, F(x,y) is the original digital image, and w is the height of F(x,y);

[0021] S22, through formula Calculate the initial baseline vector, where s is the smoothing step size;

[0022] S23. Calculate the maximum negative distance between the initial baseline vector and the projection vector using the formula max_dis=Maximum(BaseL1(y)-ProJ(y)), where Maximum() is the maximum value function.

[0023] S24. Translate BaseL1(y) to obtain BaseL2(y):

[0024] BaseL2(y) = BaseL1(y) - max_dis

[0025] S25. Let B represent the calculation process from BaseL1(y) to BaseL2(y), then:

[0026] BaseL(y) = Iter n (B)

[0027] In the formula, Iter n () represents an iterative calculation function, where n is the number of iterations;

[0028] S26, through formula Calculate the decomposed vector;

[0029] S27. Decompose the original digital image F(x,y) according to BaseL(y) to obtain the illumination image L(x,y) = F(x,y) * PBD(y) after filtering out the detection line and control line.

[0030] Furthermore, the signal enhancement process is as follows:

[0031] The original digital image F(x, y) is separated into three channels: R, G, and B. Each channel image is then input into a pre-constructed PBD function to obtain the illumination map L. R (x, y), L G (x, y) and L B (x, y), where the constructed PBD function operation process is the image illumination estimation operation process; based on image relationship analysis, we can obtain

[0032]

[0033] Taking the logarithm of both sides, we get...

[0034]

[0035] r R (x, y), r G (x, y) and r B (x, y) represent the signal diagrams restored from the three channels, which are then transformed from the logarithmic domain to the real domain to obtain R. R (x, y), R G(x, y) and R B (x, y) are then fused to obtain an enhanced fluorescence intensity image R(x, y).

[0036] Furthermore, the process of calculating the strength is as follows:

[0037] The restored fluorescence intensity image R(x, y) is projected along the chromatographic direction of the test strip to obtain the projection curve and projection vector P(i), where i is the length coordinate of the chromatographic direction image.

[0038] Calculate the first derivative P′(i) of the projection vector P(i), extract the zeros i1 and i2 between two pairs of “peak-trough” in P′(i), extract the corresponding peaks P(i1) and P(i2), take the larger value as the fluorescence intensity value of the control line C, and the smaller value as the fluorescence intensity value of the detection line T; perform regression prediction on the concentration of pathogenic microorganisms in the sample based on the fluorescence intensity value of the T line.

[0039] The present invention also provides an image enhancement system for highly sensitive detection of weak signals in immunochromatography, the system comprising:

[0040] The image illuminance estimation module is used to project the digital image of the fluorescent quantum dot immunochromatographic test strip along the longitudinal direction of the test strip and solve for the illuminance image using the basis vector decomposition function;

[0041] The signal enhancement module is used to separate the digital image into three channels: R, G, and B. The illumination image of each channel is removed to obtain the fluorescence signal image of each channel. The fluorescence signal images of the three channels are processed and fused to obtain an enhanced fluorescence intensity image.

[0042] The intensity calculation module is used to project the enhanced fluorescence intensity image, read the peak value of the projection curve, obtain the fluorescence intensity values ​​of the control line C and the detection line T of the test strip, and predict the sample concentration based on the T line.

[0043] Furthermore, the image illumination estimation module is preceded by an image relationship analysis module, which is used for:

[0044] Digital images of fluorescent quantum dot immunochromatographic test strips were acquired, and the relationship between the digital images, illumination images, and fluorescence signal images was obtained.

[0045] F(x,y)=R(x,y)·L(x,y)

[0046] Deformation

[0047]

[0048] Among them, the fluorescence intensity image R(x,y) is the excitation light information of the quantum dot, that is, the true attribute of fluorescence intensity, and the illuminance image L(x,y) is the diffuse reflection light information generated by the reflected light of fluorescence and the incident blue-green light, which is the dynamic interference information of the image.

[0049] Furthermore, the image illumination estimation module is also used for:

[0050] S21. The light intensity in the test strip image is linearly distributed along the longitudinal direction of the nitrocellulose membrane. Therefore, projecting the image longitudinally yields the brightness distribution of the image. The projection transformation formula is:

[0051]

[0052] In the formula, ProJ(y) is the projection vector, F(x,y) is the original digital image, and w is the height of F(x,y);

[0053] S22, through formula Calculate the initial baseline vector, where s is the smoothing step size;

[0054] S23. Calculate the maximum negative distance between the initial baseline vector and the projection vector using the formula max_dis=Maximum(BaseL1(y)-ProJ(y)), where Maximum() is the maximum value function.

[0055] S24. Translate BaseL1(y) to obtain BaseL2(y):

[0056] BaseL2(y) = BaseL1(y) - max_dis

[0057] S25. Let B represent the calculation process from BaseL1(y) to BaseL2(y), then:

[0058] BaseL(y) = Iter n (B)

[0059] In the formula, Iter n () represents an iterative calculation function, where n is the number of iterations;

[0060] S26, through formula Calculate the decomposed vector;

[0061] S27. Decompose the original digital image F(x,y) according to BaseL(y) to obtain the illumination image L(x,y) = F(x,y) * PBD(y) after filtering out the detection line and control line.

[0062] Furthermore, the signal enhancement module is also used for:

[0063] The original digital image F(x, y) is separated into three channels: R, G, and B. Each channel image is then input into a pre-constructed PBD function to obtain the illumination map L. R (x, y), L G (x, y) and L B (x, y), where the constructed PBD function operation process is the image illumination estimation operation process; based on image relationship analysis, we can obtain

[0064]

[0065] Taking the logarithm of both sides, we get...

[0066]

[0067] r R (x, y), r G (x, y) and r B (x, y) represent the signal diagrams restored from the three channels, which are then transformed from the logarithmic domain to the real domain to obtain R. R (x, y), R G (x, y) and R B (x, y) are then fused to obtain an enhanced fluorescence intensity image R(x, y).

[0068] Furthermore, the strength calculation module is also used for:

[0069] The restored fluorescence intensity image R(x, y) is projected along the chromatographic direction of the test strip to obtain the projection curve and projection vector P(i), where i is the length coordinate of the chromatographic direction image.

[0070] Calculate the first derivative P′(i) of the projection vector P(i), extract the zeros i1 and i2 between two pairs of “peak-trough” in P′(i), extract the corresponding peaks P(i1) and P(i2), take the larger value as the fluorescence intensity value of the control line C, and the smaller value as the fluorescence intensity value of the detection line T; perform regression prediction on the concentration of pathogenic microorganisms in the sample based on the fluorescence intensity value of the T line.

[0071] The advantages of this invention are:

[0072] (1) By estimating the image illuminance of the digital image of the immunochromatographic test strip, the illuminance image is quickly decomposed. Then, the digital image is separated into three channels: R, G, and B. The illuminance image of each channel is removed to obtain the fluorescence signal image of each channel. The fluorescence signal image of each channel is processed and fused to obtain an enhanced fluorescence intensity image. Then, the control line and detection line of the test strip are obtained using the enhanced fluorescence intensity image. Since the fluorescence intensity image is enhanced, the intensity of the quantum dot immunochromatographic signal can be detected quickly and accurately even for low-concentration samples, with high sensitivity.

[0073] (2) The algorithm of the present invention can reduce the contrast between fluorescence signal and image background noise, and improve the visual and machine recognition of weak fluorescence signal.

[0074] (3) The algorithm of the present invention can replace the filter, avoid the light flux loss caused by the filter, and maintain the brightness gradient. Attached Figure Description

[0075] Figure 1 This is a flowchart of an image enhancement method for highly sensitive detection of weak signals in immunochromatography, as disclosed in Embodiment 1 of the present invention;

[0076] Figure 2 The image shown is the original test strip image in the image enhancement method for highly sensitive detection of weak signals in immunochromatography disclosed in Embodiment 1 of the present invention.

[0077] Figure 3 This is a schematic diagram illustrating the enhancement effect of the test strip number image in the image enhancement method for highly sensitive detection of weak signals in immunochromatography disclosed in Embodiment 1 of the present invention;

[0078] Figure 4 This is a schematic diagram comparing the signal projection curves before and after fluorescence intensity image enhancement in an image enhancement method for highly sensitive detection of weak signals in immunochromatography, as disclosed in Embodiment 1 of the present invention. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0080] Example 1

[0081] like Figure 1 As shown, an image enhancement method for highly sensitive detection of weak signals in immunochromatography is provided, the method comprising:

[0082] S1. Image Relationship Analysis: Digital images of the fluorescent quantum dot immunochromatographic test strip are acquired, and the relationship between the digital images, illumination images, and fluorescence signal images is obtained. The test strip is an immunochromatographic test strip based on CdSe elemental quantum dots of various colors. The original test strip image is shown below. Figure 2 As shown. The specific process of image relationship analysis is as follows:

[0083] Digital images of fluorescent quantum dot immunochromatographic test strips were acquired, and the relationship between the digital images, illumination images, and fluorescence signal images was obtained.

[0084] F(x,y)=R(x,y)·L(x,y)

[0085] Deformation

[0086]

[0087] Among them, the fluorescence intensity image R(x,y) is the excitation light information of the quantum dot, that is, the true attribute of fluorescence intensity, and the illuminance image L(x,y) is the diffuse reflection light information generated by the reflected light of fluorescence and the incident blue-green light, which is the dynamic interference information of the image.

[0088] S2. Image illuminance estimation: The digital image of the fluorescent quantum dot immunochromatographic test strip is projected along the longitudinal direction of the test strip, and the illuminance image is solved using a basis vector decomposition function; the process of image illuminance estimation is as follows:

[0089] S21. The light intensity in the test strip image is linearly distributed along the longitudinal direction of the nitrocellulose membrane. Therefore, projecting the image longitudinally yields the brightness distribution of the image. The projection transformation formula is:

[0090]

[0091] In the formula, ProJ(y) is the projection vector, F(x,y) is the original digital image, and w is the height of F(x,y);

[0092] S22, through formula Calculate the initial baseline vector, where s is the smoothing step size, and we take s = 0.4 * L(ProJ(y)), where L(ProJ(y)) is the length of ProJ(y);

[0093] S23. Calculate the maximum negative distance between the initial baseline vector and the projection vector using the formula max_dis=Maximum(BaseL1(y)-ProJ(y)), where Maximum() is the maximum value function.

[0094] S24. Translate BaseL1(y) to obtain BaseL2(y):

[0095] BaseL2(y) = BaseL1(y) - max_dis

[0096] S25. Let B represent the calculation process from BaseL1(y) to BaseL2(y), then:

[0097] BaseL(y) = Iter n (B)

[0098] In the formula, Iter n () represents an iterative calculation function, where n is the number of iterations. Take n=3. For example, when calculating BaseL3(y), BaseL3(y) = BaseL2(y) - max_dis, and max_dis = Maximum(BaseL2(y) - ProJ(y)).

[0099] S26, through formula Calculate the decomposed vector;

[0100] S27. Decompose the original digital image F(x,y) according to BaseL(y) to obtain the illumination image L(x,y) = F(x,y) * PBD(y) after filtering out the detection line and control line.

[0101] S3. Signal Enhancement: The digital image is separated into three channels (R, G, and B). The illuminance image of each channel is removed to obtain the fluorescence signal image of each channel. The fluorescence signal images of the three channels are processed and fused to obtain an enhanced fluorescence intensity image. The signal-enhanced test strip image is shown below. Figure 3 As shown, the signal strength is higher after image enhancement compared to before enhancement, and a clear red mark can be displayed at the detection line, indicating higher sensitivity compared to before image enhancement. The signal enhancement process is as follows:

[0102] The original digital image F(x, y) is separated into three channels: R, G, and B. Each channel image is then input into a pre-constructed PBD function to obtain the illumination map L. R (x, y), L G (x, y) and L B (x, y), where the constructed PBD function operation process is the image illumination estimation operation process; based on image relationship analysis, we can obtain

[0103]

[0104] Taking the logarithm of both sides, we get...

[0105]

[0106] r R (x, y), r G(x, y) and r B (x, y) represent the signal diagrams restored from the three channels, which are then transformed from the logarithmic domain to the real domain to obtain R. R (x, y), R G (x, y) and R B (x, y) are then fused to obtain an enhanced fluorescence intensity image R(x, y).

[0107] S4. Intensity calculation: Project the enhanced fluorescence intensity image, read the peak value of the projection curve, obtain the fluorescence intensity values ​​of the control line (C line) and the detection line (T line) of the test strip, and predict the sample concentration based on the T line; the intensity calculation process is as follows:

[0108] The restored fluorescence intensity image R(x, y), which is also the enhanced fluorescence intensity image R(x, y), is projected along the chromatographic direction of the test strip to obtain the projection curve and projection vector P(i), where i is the length coordinate of the chromatographic direction image.

[0109] Calculate the first derivative P′(i) of the projection vector P(i), extract the zeros i1 and i2 between two pairs of “peak-trough” in P′(i), extract the corresponding peaks P(i1) and P(i2), take the larger value as the fluorescence intensity value of the control line C, and the smaller value as the fluorescence intensity value of the detection line T; perform regression prediction on the concentration of pathogenic microorganisms in the sample based on the fluorescence intensity value of the T line.

[0110] The present invention provides a comparison of signal projection curves of the test strip before and after fluorescence intensity image enhancement, for example. Figure 4 As shown, Figure 4 The dashed line represents the enhanced fluorescence intensity image, while the solid line represents the original fluorescence intensity image. Taking the enhanced fluorescence intensity image as an example, the methods for obtaining the control line C and the detection line T will be explained. First, the derivative of the dashed line in the figure, i.e., the enhanced fluorescence intensity image, is calculated. Figure 4 The three points of the four-pointed star will successively become zero point i. 11 Zero point i 12 Zero point i 13 Collectively referred to as zero point i1, while zero point i 11 Zero point i 12 Zero point i 13 A pair of peaks and troughs will be formed between them; similarly, Figure 4 The three circles in the middle will successively become zero point i. 21 Zero point i 22 Zero point i 23 Collectively referred to as zero point i2, while zero point i 21 Zero point i 22 Zero point i 23 Another pair of peaks and troughs will be formed between them, thus in the extraction of value P(i) 11), P(i 12 ), P(i 13 The largest of the three values ​​is taken as the peak P(i1), and the extracted value P(i) is taken as the peak value P(i1). 21 ), P(i 22 ), P(i 23 The largest of the three values ​​is taken as another peak P(i2), the larger of P(i1) and P(i2) is taken as the fluorescence intensity value of the control line C, and the smaller value is taken as the fluorescence intensity value of the detection line T. For example Figure 4 The value of the dot marked with a four-pointed star in the middle corresponds to the fluorescence intensity value of the control line C, and the value of the dot marked with a circle in the middle corresponds to the fluorescence intensity value of the detection line T.

[0111] Through the above technical solution, this invention estimates the illuminance of the digital image of the immunochromatographic test strip, quickly decomposes the illuminance image, then separates the digital image into three channels: R, G, and B. The illuminance image of each channel is removed to obtain the fluorescence signal image of each channel. These images are then processed and fused to obtain an enhanced fluorescence intensity image. The enhanced fluorescence intensity image is then used to obtain the control line and detection line of the test strip. Because the fluorescence intensity image is enhanced, the intensity of the quantum dot immunochromatographic signal can be detected quickly and accurately even for low-concentration samples, exhibiting high sensitivity.

[0112] Example 2

[0113] Based on Embodiment 1, Embodiment 2 of the present invention also provides an image enhancement system for highly sensitive detection of weak signals in immunochromatography, the system comprising:

[0114] The image illuminance estimation module is used to project the digital image of the fluorescent quantum dot immunochromatographic test strip along the longitudinal direction of the test strip and solve for the illuminance image using the basis vector decomposition function;

[0115] The signal enhancement module is used to separate the digital image into three channels: R, G, and B. The illumination image of each channel is removed to obtain the fluorescence signal image of each channel. The fluorescence signal images of the three channels are processed and fused to obtain an enhanced fluorescence intensity image.

[0116] The intensity calculation module is used to project the enhanced fluorescence intensity image, read the peak value of the projection curve, obtain the fluorescence intensity values ​​of the control line C and the detection line T of the test strip, and predict the sample concentration based on the T line.

[0117] Specifically, the image illumination estimation module is preceded by an image relationship analysis module, which is used for:

[0118] Digital images of fluorescent quantum dot immunochromatographic test strips were acquired, and the relationship between the digital images, illumination images, and fluorescence signal images was obtained.

[0119] F(x,y)=R(x,y)·L(x,y)

[0120] Deformation

[0121]

[0122] Among them, the fluorescence intensity image R(x,y) is the excitation light information of the quantum dot, that is, the true attribute of fluorescence intensity, and the illuminance image L(x,y) is the diffuse reflection light information generated by the reflected light of fluorescence and the incident blue-green light, which is the dynamic interference information of the image.

[0123] More specifically, the image illumination estimation module is also used for:

[0124] S21. The light intensity in the test strip image is linearly distributed along the longitudinal direction of the nitrocellulose membrane. Therefore, projecting the image longitudinally yields the brightness distribution of the image. The projection transformation formula is:

[0125]

[0126] In the formula, ProJ(y) is the projection vector, F(x,y) is the original digital image, and w is the height of F(x,y);

[0127] S22, through formula Calculate the initial baseline vector, where s is the smoothing step size;

[0128] S23. Calculate the maximum negative distance between the initial baseline vector and the projection vector using the formula max_dis=Maximum(BaseL1(y)-ProJ(y)), where Maximum() is the maximum value function.

[0129] S24. Translate BaseL1(y) to obtain BaseL2(y):

[0130] BaseL2(y) = BaseL1(y) - max_dis

[0131] S25. Let B represent the calculation process from BaseL1(y) to BaseL2(y), then:

[0132] BaseL(y) = Iter n (B)

[0133] In the formula, Iter n () represents an iterative calculation function, where n is the number of iterations;

[0134] S26, through formula Calculate the decomposed vector;

[0135] S27. Decompose the original digital image F(x,y) according to BaseL(y) to obtain the illumination image L(x,y) = F(x,y) * PBD(y) after filtering out the detection line and control line.

[0136] More specifically, the signal enhancement module is also used for:

[0137] The original digital image F(x, y) is separated into three channels: R, G, and B. Each channel image is then input into a pre-constructed PBD function to obtain the illumination map L. R (x, y), L G (x, y) and L B (x, y), where the constructed PBD function operation process is the image illumination estimation operation process; based on image relationship analysis, we can obtain

[0138]

[0139] Taking the logarithm of both sides, we get...

[0140]

[0141]

[0142] r R (x, y), r G (x, y) and r B (x, y) represent the signal diagrams restored from the three channels, which are then transformed from the logarithmic domain to the real domain to obtain R. R (x, y), R G (x, y) and R B (x, y) are then fused to obtain an enhanced fluorescence intensity image R(x, y).

[0143] More specifically, the strength calculation module is also used for:

[0144] The restored fluorescence intensity image R(x, y) is projected along the chromatographic direction of the test strip to obtain the projection curve and projection vector P(i), where i is the length coordinate of the chromatographic direction image.

[0145] Calculate the first derivative P′(i) of the projection vector P(i), extract the zeros i1 and i2 between two pairs of “peak-trough” in P′(i), extract the corresponding peaks P(i1) and P(i2), take the larger value as the fluorescence intensity value of the control line C, and the smaller value as the fluorescence intensity value of the detection line T; perform regression prediction on the concentration of pathogenic microorganisms in the sample based on the fluorescence intensity value of the T line.

[0146] Example 3

[0147] Based on Embodiment 1, Embodiment 3 of the present invention also provides an electronic processing device, including at least one processor and a storage device storing at least one executable program. When the at least one executable program is executed by the at least one processor, the at least one processor implements the following method:

[0148] Image illuminance estimation involves projecting the digital image of the fluorescent quantum dot immunochromatographic test strip along the longitudinal direction of the test strip and solving for the illuminance image using a basis vector decomposition function.

[0149] Signal enhancement involves separating the digital image into three channels: R, G, and B. The illuminance image of each channel is removed to obtain the fluorescence signal image of each channel. The fluorescence signal images of the three channels are then processed and fused to obtain an enhanced fluorescence intensity image.

[0150] Intensity calculation involves projecting the enhanced fluorescence intensity image, reading the peak value of the projection curve, obtaining the fluorescence intensity values ​​of the control line (C line) and the detection line (T line) of the test strip, and predicting the sample concentration based on the T line.

[0151] Example 4

[0152] Based on Embodiment 1, Embodiment 4 of the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the following method:

[0153] Image illuminance estimation involves projecting the digital image of the fluorescent quantum dot immunochromatographic test strip along the longitudinal direction of the test strip and solving for the illuminance image using a basis vector decomposition function.

[0154] Signal enhancement involves separating the digital image into three channels: R, G, and B. The illuminance image of each channel is removed to obtain the fluorescence signal image of each channel. The fluorescence signal images of the three channels are then processed and fused to obtain an enhanced fluorescence intensity image.

[0155] Intensity calculation involves projecting the enhanced fluorescence intensity image, reading the peak value of the projection curve, obtaining the fluorescence intensity values ​​of the control line (C line) and the detection line (T line) of the test strip, and predicting the sample concentration based on the T line.

[0156] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An image enhancement method for highly sensitive detection of weak signals in immunochromatography, characterized in that, The method includes: Image illuminance estimation involves projecting the digital image of the fluorescent quantum dot immunochromatographic test strip along the longitudinal direction of the strip and solving for the illuminance image using a basis vector decomposition function. The process of image illuminance estimation is as follows: S21. The light intensity in the test strip image is linearly distributed along the longitudinal direction of the nitrocellulose membrane. Therefore, projecting the image longitudinally yields the brightness distribution of the image. The projection transformation formula is: In the formula, For projection vectors, For the original digital image, w is Height; S22, through formula Calculate the initial baseline vector, where s is the smoothing step size; S23, through formula Calculate the maximum negative distance between the initial baseline vector and the projection vector, where, ( ) represents the function that takes the maximum value; S24, Translation BaseL1 Get BaseL2 : S25. Let B represent BaseL1. To BaseL2 The calculation process is as follows: In the formula, This represents an iterative computation function, where n is the number of iterations. S26, through formula Calculate the decomposed vector; S27, according to For the original digital image The image was decomposed to obtain an illumination image after filtering out the detection line and control line. ; Signal enhancement involves separating the digital image into three channels: R, G, and B. The illuminance image of each channel is removed to obtain the fluorescence signal image of each channel. The fluorescence signal images of the three channels are then processed and fused to obtain an enhanced fluorescence intensity image. Intensity calculation involves projecting the enhanced fluorescence intensity image, reading the peak value of the projection curve, obtaining the fluorescence intensity values ​​of the control line (C line) and the detection line (T line) of the test strip, and predicting the sample concentration based on the T line.

2. The image enhancement method for highly sensitive detection of weak signals in immunochromatography according to claim 1, characterized in that, The image illumination estimation is preceded by image relationship analysis, the process of which is as follows: Digital images of fluorescent quantum dot immunochromatographic test strips were acquired, and the relationship between the digital images, illumination images, and fluorescence signal images was obtained. Deformation Among them, fluorescence intensity image For the excitation light information of quantum dots, i.e., the true property of fluorescence intensity, illuminance image. This represents the information of diffuse reflection generated by the reflected light of fluorescence and the incident blue-green light, and the dynamic interference information of the image.

3. The image enhancement method for highly sensitive detection of weak signals in immunochromatography according to claim 1, characterized in that, The signal enhancement process is as follows: Original digital image The image is separated into three channels: R, G, and B. Each channel image is then input into the constructed PBD function to obtain the illumination map. , and The constructed PBD function operation process is the image illumination estimation operation process; based on image relationship analysis, it can be obtained that... Taking the logarithm of both sides, we get... , and The signal diagrams for the three channels are restored, and their transformation from the logarithmic domain to the real domain is obtained. , and Then, the images are fused to obtain an enhanced fluorescence intensity image R. .

4. The image enhancement method for highly sensitive detection of weak signals in immunochromatography according to claim 3, characterized in that, The process of calculating the strength is as follows: The restored fluorescence intensity image Projecting along the chromatographic direction of the test strip yields the projection curve and projection vector. , These are the length coordinates of the tomographic direction image; Find the projection vector first derivative ,extract The zero point between two pairs of "peaks and troughs" and Extract the corresponding peak values and The larger value is used as the fluorescence intensity value of the control line C, and the smaller value is used as the fluorescence intensity value of the detection line T. The concentration of pathogenic microorganisms in the sample is predicted by regression based on the fluorescence intensity value of the T line.

5. An image enhancement system for highly sensitive detection of weak signals in immunochromatography, characterized in that, The system includes: The image illuminance estimation module is used to project the digital image of the fluorescent quantum dot immunochromatographic test strip along the longitudinal direction of the test strip and solve for the illuminance image using a basis vector decomposition function; the image illuminance estimation module is also used for: S21. The light intensity in the test strip image is linearly distributed along the longitudinal direction of the nitrocellulose membrane. Therefore, projecting the image longitudinally yields the brightness distribution of the image. The projection transformation formula is: In the formula, For projection vectors, For the original digital image, w is Height; S22, through formula Calculate the initial baseline vector, where s is the smoothing step size; S23, through formula Calculate the maximum negative distance between the initial baseline vector and the projection vector, where, ( ) represents the function that takes the maximum value; S24, Translation BaseL1 Get BaseL2 : S25. Let B represent BaseL1. To BaseL2 The calculation process is as follows: In the formula, This represents an iterative computation function, where n is the number of iterations. S26, through formula Calculate the decomposed vector; S27, according to For the original digital image The image was decomposed to obtain an illumination image after filtering out the detection line and control line. ; The signal enhancement module is used to separate the digital image into three channels: R, G, and B. The illuminance image of each channel is removed to obtain the fluorescence signal image of each channel. The fluorescence signal images of the three channels are processed and fused to obtain an enhanced fluorescence intensity image. The intensity calculation module is used to project the enhanced fluorescence intensity image, read the peak value of the projection curve, obtain the fluorescence intensity values ​​of the control line C and the detection line T of the test strip, and predict the sample concentration based on the T line.

6. The image enhancement system for highly sensitive detection of weak signals in immunochromatography according to claim 5, characterized in that, The image illumination estimation module is preceded by an image relationship analysis module, which is used for: Digital images of fluorescent quantum dot immunochromatographic test strips were acquired, and the relationship between the digital images, illumination images, and fluorescence signal images was obtained. Deformation Among them, fluorescence intensity image For the excitation light information of quantum dots, i.e., the true property of fluorescence intensity, illuminance image. This represents the information of diffuse reflection generated by the reflected light of fluorescence and the incident blue-green light, and the dynamic interference information of the image.

7. The image enhancement system for highly sensitive detection of weak signals in immunochromatography according to claim 5, characterized in that, The signal enhancement module is also used for: Original digital image The image is separated into three channels: R, G, and B. Each channel image is then input into the constructed PBD function to obtain the illumination map. , and The constructed PBD function operation process is the image illumination estimation operation process; based on image relationship analysis, it can be obtained that... Taking the logarithm of both sides, we get... , and The signal diagrams for the three channels are restored, and their transformation from the logarithmic domain to the real domain is obtained. , and Then, the images are fused to obtain an enhanced fluorescence intensity image R. .

8. The image enhancement system for highly sensitive detection of weak signals in immunochromatography according to claim 7, characterized in that, The strength calculation module is also used for: The restored fluorescence intensity image Projecting along the chromatographic direction of the test strip yields the projection curve and projection vector. , These are the length coordinates of the tomographic direction image; Find the projection vector first derivative ,extract The zero point between two pairs of "peaks and troughs" and Extract the corresponding peak values and The larger value is used as the fluorescence intensity value of the control line C, and the smaller value is used as the fluorescence intensity value of the detection line T. The concentration of pathogenic microorganisms in the sample is predicted by regression based on the fluorescence intensity value of the T line.

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