Assay result image capturing and analysis system and method

Abstract

A method for assay result analysis for a given application, the method comprising the steps of acquiring an image of the assay, applying saturation intensity matching to the acquired image using a correction function built using a reference color gradient specific to the given application, resulting in a corrected saturation image, applying dynamic scoping of a given region of interest using mathematical thresholding to the corrected saturation image, and then identifying the presence or absence of pixel rectangles in the given region of interest. There is also disclosed a system performing the method for assay result analysis.

Description

ASSAY RESULT IMAGE CAPTURING AND ANALYSIS SYSTEM AND METHODCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefits of U.S. provisional patent application No. 63 / 667,453 filed on July 3, 2024, which is herein incorporated by reference.TECHNICAL FIELD

[0002] The present disclosure generally relates to bodily fluid (biofluid) sample collection, testing and analysis. More specifically, but not exclusively the present disclosure relates to an assay result image capturing and analysis system and method. Still more particularly but still not exclusively, the present disclosure relates to a colorimetric analysis solution based on user-provided mobile devices.BACKGROUND

[0003] Biofluid sample collection, such as saliva sampling, is a common step for many analytical tests. This spans across a number of technical fields and is notably used in medical diagnostic tests. Human fluid samples may consist of a number of fluid types (saliva, blood, urine, mucus, etc.). Sample collection is conducted in a number of ways, whether by a medical professional sampling a patient or the patient being given the necessary tools to retrieve the sample themselves. This may be done with a swab, a container, or other similar instruments. For example, a patient may be instructed to deposit urine into a container, which can be transported to a laboratory for analysis. Samples are often analyzed usinglaboratory-grade tools and equipment, which are typically expensive and difficult to acquire. The common layperson does not usually have access to specialized equipment and is required to visit a clinic or hospital in order to receive a liquid sample test and analysis results.

[0004] Point of care medicine is increasing the common person’s access to medical results. This field of medicine focuses on simple medical testing that can be conducted by a layperson in their home or similar unregulated environment. At- home liquid sample collection devices will reduce the cost and wait time of a sample analysis, and additionally reduce the risk present in the transportation and storage that is required to analyze a sample with typical laboratory methods.

[0005] A common method of applying point of care medicine is through lateral flow assays (LFA) or lateral flow immunoassay, a known technology for the testing of the presence of a specific reactant within the human body (i.e. Covid-19 test strips, pregnancy tests etc.). These tests are low-cost to manufacture, can be customized for very specific testing purposes, and do not require large liquid samples in order to generate a result. A lateral flow immunoassay is a simple diagnostic device that confirms the presence or absence of a specific analyte. The principle of an LFA is based on the movement of a liquid sample though a polymeric strip with attached molecules that interact with the analyte, providing a signal that can be visually detected. All immunoassays share the same fundamental components: an analyte, basically the target molecule that is to be detected (e.g. progesterone, cortisol, covid etc.), an analyte-specific capturing reagent (e.g. antibody, aptamer, protein etc.) to capture or to bind to the analyte, a detectable label attached (or tagged) to the capturing reagent (e.g. chromophore, fluorophore etc.). The analyte-specific capturing reagent and the detectable label attached thereto form a conjugate.

[0006] Various devices and methods have been developed to confirm the presence or absence of a specific analyte, but such solutions require laboratory grade instruments, external hardware, a controlled environment and / or complex and lengthy procedures from the user’s perspective. As such, they pose many drawbacks, such as lack of accessibility due to price and / or availability, excessive procedural complexity, nontrivial learning curve, and / or ack of robustness.

[0007] These problems highlight the need for a more accessible, user- friendly, and robust solution from material and technical perspectives. It also highlights the need for a scalable solution from a commercial point of view to democratize access.OBJECTS

[0008] An object of the present disclosure is to provide a system and method for analyzing biofluids.

[0009] An object of the present disclosure is to provide an assay result image capturing and analysis system and method.

[0010] An object of the present disclosure is to provide an assay result image capturing and analysis system and method based on the principles of saturation intensity matching and threshold filling between a reference system and user supplied systems specifically targeted at gold nanoparticle dyes.

[0011] An object of the present disclosure is to provide a colorimetric analysis solution based on user-provided mobile devices.SUMMARY

[0012] In accordance with an aspect of the present disclosure, there is provided a method for assay result analysis for a given application, the method comprising the steps of:

[0013] a) acquiring an image of the assay;

[0014] b) applying saturation intensity matching to the acquired image using a correction function built using a reference color gradient specific to the given application, resulting in a corrected saturation image;

[0015] c) applying dynamic scoping of a given region of interest using mathematical thresholding to the corrected saturation image; and

[0016] d) identifying the presence or absence of pixel rectangles in the given region of interest.

[0017] In an embodiment of this aspect, prior to step b) is performed the step consisting of: aa) adjusting a temperature of the acquired image based on a reference temperature.

[0018] In another embodiment of this aspect, step aa) consists of the substeps of:

[0019] i) obtaining a temperature RGB triplet by computing a mean value of red, green and blue channels across all pixels of the acquired image;

[0020] ii) converting the temperature RGB triplet into a correlated color temperature expressed in Kelvin by applying a colorimetric transformation to the temperature RGB triplet;

[0021] ill) normalizing the transformed temperature RGB triplet;

[0022] iv) estimating the color temperature by applying a procedure selected from a group consisting of an interpolation of empirical data procedure, a curve fitting procedure, a computational model of blackbody radiation procedure and a standard illuminants procedure to determine the most likely Kelvin temperature that would produce a similar RGB balance to the temperature RGB triplet under standardized lighting conditions; and

[0023] v) adjusting the color temperature of the acquired image to the reference temperature using standardized tables published by the International Commission on Illumination.

[0024] In a further embodiment of this aspect, prior to step b) is performed the step consisting of: ab) adjusting a brightness of the acquired image based on a reference brightness.

[0025] In an embodiment of this aspect, step ab) consists of the sub-steps of:

[0026] i) obtaining a brightness RGB triplet by computing a mean pixel intensity of a luminance-preserved grayscale representation of the acquired image; and

[0027] ii) adjusting the brightness of the acquired image to the reference brightness using a brightness scaling factor computed as a ratio of the brightness RGB triplet over the reference brightness.

[0028] In another embodiment of this aspect, the image of the assay is acquired from an image capture device, and wherein the saturation intensity matching is further based on a mean absolute error linear regression between a reference saturation level of a given reference color pixel from a reference imagecapture device and a saturation level of a corresponding color pixel from the image capture device.

[0029] In a further embodiment of this aspect, the reference saturation level for each given reference color pixel is built from colors observed in images of tests assays for the given application.

[0030] In a further embodiment of this aspect, the essay is selected from a group consisting of a lateral flow assay and a lateral flow immunoassay.

[0031] In accordance with another aspect of the present disclosure, there is provided a computing device program product comprising a computing device readable memory storing computing device executable instructions thereon that when executed by a computing device perform the method steps described above.

[0032] In accordance with yet another aspect of the present disclosure, there is provided a system for assay result analysis for a given application, the system comprising:

[0033] an input interface for acquiring an assay image;

[0034] at least one processor operatively connected to the input interface, the at least one processor having an associated memory having stored therein processor executable code that when executed by the at least one processor performs the steps of:

[0035] a) obtain the assay image from the input interface;

[0036] b) apply saturation intensity matching to the obtained assay image using a correction function built using a reference color gradient specific to the given application, resulting in a corrected saturation image;

[0037] c) apply dynamic scoping of a given region of interest using mathematical thresholding to the corrected saturation image; and

[0038] d) identify the presence or absence of pixel rectangles in the given region of interest.

[0039] In an embodiment of this aspect, the image of the assay is acquired from an image capture device via the input interface, and wherein the saturation intensity matching is further based on a mean absolute error linear regression between a reference saturation level of a given reference color pixel from a reference image capture device and a saturation level of a corresponding color pixel from the image capture device.

[0040] In another embodiment of this aspect, the system further comprises an image capture device operatively connected to the input interface.

[0041] In a further embodiment of this aspect, the saturation intensity matching is based on a mean absolute error linear regression between a reference saturation level of a given reference color pixel from a reference image capture device and a saturation level of a corresponding color pixel from the image capture device.

[0042] In an embodiment of this aspect, the reference saturation level for each given reference color pixel is built from colors observed in images of tests assays for the given application.

[0043] In another embodiment of this aspect, prior to step b) the at least one processor performs the step consisting of: aa) adjusting a temperature of the acquired image based on a reference temperature.

[0044] In a further embodiment of this aspect, step aa) consists of the substeps of:

[0045] i) obtaining a temperature RGB triplet by computing a mean value of red, green and blue channels across all pixels of the acquired image;

[0046] ii) converting the temperature RGB triplet into a correlated color temperature expressed in Kelvin by applying a colorimetric transformation to the temperature RGB triplet;

[0047] ill) normalizing the transformed temperature RGB triplet;

[0048] iv) estimating the color temperature by applying a procedure selected from a group consisting of an interpolation of empirical data procedure, a curve fitting procedure, a computational model of blackbody radiation procedure and a standard illuminants procedure to determine the most likely Kelvin temperature that would produce a similar RGB balance to the temperature RGB triplet under standardized lighting conditions; and

[0049] v) adjusting the color temperature of the acquired image to the reference temperature using standardized tables published by the International Commission on Illumination.

[0050] In an embodiment of this aspect, prior to step b) the at least one processor performs the step consisting of: ab) adjusting a brightness of the acquired image based on a reference brightness.

[0051] In another embodiment of this aspect, step ab) consists of the substeps of:

[0052] i) obtaining a brightness RGB triplet by computing a mean pixel intensity of a luminance-preserved grayscale representation of the acquired image; and

[0053] ii) adjusting the brightness of the acquired image to the reference brightness using a brightness scaling factor computed as a ratio of the brightness RGB triplet over the reference brightness.

[0054] Other objects, advantages and features of the present disclosure will become more apparent upon reading of the following non-restrictive description of illustrative embodiments thereof, given by way of example only with reference to the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In the appended drawings:

[0056] Figure 1 is a schematic representation of the assay result image capturing and analysis system in accordance with an in accordance with a non- restrictive illustrative embodiment of the present disclosure;

[0057] Figures 2A and 2B are flow diagrams depicting the assay result image capturing and analysis process in accordance with the illustrative embodiment of the present disclosure (Figure 2A) and the optional temperature and brightness adjustment sub-process (Figure 2B);

[0058] Figure 3 is a top and side perspective view of an example of a device for collecting, transferring, and testing biofluids in accordance with a non-restrictive illustrative embodiment of the present disclosure;

[0059] Figures 4A and 4B are schematic representations of the saturation images of a same assay result acquired by the reference system 12 (Figure 4A) and by the user’s system 32(Figure 4B);

[0060] Figures 5A and 5B are schematic representations of the saturation image of an assay result acquired by the user’s system 42 (Figure 5A) and the saturation image after the application of the correction function (Figure 5B);

[0061] Figure 6 is a schematic representation of the correction function used to obtain Figure 5B;

[0062] Figure 7 is an example of a reference color gradient graph;

[0063] Figure 8 is a comparison between the refence gradient and the user’s saturation image graphs;

[0064] Figure 9 is a graph of a comparison between the refence and a user’s saturation image after the application of the correction function using mean saturation intensity;

[0065] Figure 10 is a graph representation of mathematical thresholding;

[0066] Figure 11 is an example of prior art user interface for selecting an area of interest in the analysis of a lateral flow assay or lateral flow immunoassay; and

[0067] Figures 12A and 12B are schematic representations of a line whose pixels comprise two columns (Figure 12A) and the same line with an extra pixel column to the right (Figure 12B), both having the same saturation sum.DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0068] Generally stated and in accordance with an aspect of the present disclosure, there is provided an assay result image capturing and analysis system and method based on the principles of saturation intensity matching and threshold filling between a reference system and user supplied systems specifically targeted at gold nanoparticle dyes. The reference device is used to gather image data to train an “oracle” in the form of a machine learning algorithm which “learns” to associate a particular numerical metric (predictor hereafter) to a quantitative analyte (e.g. cortisol and progesterone) range.

[0069] The saturation-based approach of the present disclosure is novel in its use of saturation intensity matching and a simplified analysis based on the mathematical ratio between the sum of the saturation values of “active” pixels over the sum of “inactive” pixels over a determined region of interest (ROI) of a captured image of a lateral flow assay (LFA) or lateral flow immunoassay.

[0070] Referring to Figure 1 , the assay result image capturing and analysis system 10 includes a processing unit 20 having one or more processor 22 with an associated memory 24 having stored therein one or more application specific reference gradient 26 and processor executable instructions for configuring the one or more processor 22 to execute the assay result image analysis process 100. It is to be understood that other processes, libraries and tools’ executable instructions may be stored in the memory 24 in order to support the assay result image analysis process 100. The processing unit 20 further includes an input / output (I / O) interface 28 for communication with a camera 30 and a user interface 40. The assay result image capturing and analysis system 10 may be implemented using a smart phone, tablet, laptop or any other such computing device that can be provided with a camera or is capable of acquiring an image (for example via download).

[0071] Referring now to Figure 2A, there is shown a flow diagram of the assay result image analysis process 100 in accordance with the illustrative embodiment of the present disclosure. Steps of the process 100 are indicated by blocks 102 to 108.

[0072] The process 100 starts at block 102 where the image of a lateral flow assay (LFA) or lateral flow immunoassay 2 (see Figure 3) is taken by a user, for example using camera 30 of the image capturing and analysis system 10. In an alternative embodiment, the user may download the image via the user interface 40. An example of a lateral flow assay (LFA) or lateral flow that can be used with the image capturing and analysis system 10 is disclosed in International Patent Application Number PCT / CA2024 / 050960 filed on July 19, 2024., and titled DEVICE AND METHOD FOR BIOFLUID SAMPLE COLLECTION AND TRANSFER, which is hereby included by reference in its entirety.

[0073] At block 104, optionally in the case where the image analysis process 100 has been trained on images that have been normalized to a reference brightness and / or temperature, the temperature and / or brightness adjustment subprocess is performed (which sub-process is further detailed below).

[0074] Then, at block 106, the process 100 saturation intensity matching (SIM) is applied to the image captured at block 102 (or optionally the adjusted image from block 104). The SIM is a correction method based on a mean absolute error (MAE) linear regression between the reference system’s saturation level (sre / ) for a given reference color pixel and the user-supplied system’s (e.g., system 10 with camera 30) saturation level for a similar color pixel (suser). Linear regression is performed on the error s = sref- suserover a predetermined reference image. The resulting model (<p) allows the process 100 to calculate the estimated error between the user’s assay result image capturing and analysis system 10 and the reference system in the form of a correction function A = <p(suser) which could be rewritten as(Puser^suser)tosignify that this model only applies to a specific system 10. In an alternative embodiment, the assay result image capturing and analysis system 10 could be provided with a plurality of processes 100 and associated reference gradients 26, each being applicable for a specific application, which could be selected via the user interface 40.

[0075] Reference is made to Figures 4A, 4B, 5A and 5B to illustrate the saturation intensity matching step 106 of process 100. In Figures 5A and 5B there are shown schematic representations of the saturation images of a same assay result acquired by the reference system 12 (Figure 4A) and by the user’s system 32 (Figure 4B), highlighting the differences between distinct systems (i.e. , cameras). In Figure 5A there is shown a schematic representation of the saturation image of an assay result acquired by the user’s system 42 and, in Figure 5B, the saturation image 52 after the application of the correction function <p = -0.5suser, which gives 8 - 0.5 * 8 = 4, 10 - 0.5 • 10 = 5, 12 - 0.5 • 12 = 6 and 14 - 0.5 * 14 = 7. Figure 6 shows a schematic representation of the application of the correction function <p).

[0076] Saturation image 52 is an estimation of the saturation image the reference system would acquire if it was used instead of the user’s system 10 (i.e., camera 30). As such, it diminishes inter-system variability and provides process 100 with an example that is representative of the images that were used to “train” it.

[0077] Referring now to Figure 7, the corrector function <p) is built using a reference color gradient 26 taken from colors observed in actual tests which makes it application specific. As mentioned previously, in the alternative embodiment where the assay result image capturing and analysis system 10 is provided with a plurality of processes 100 and associated reference gradients 26 for multiple specific application, each application will be provided with a corresponding specific corrector function.

[0078] At Figure 8, the refence gradient 26 is compared to the user’s saturation image 52 after the application of the correction function using mean saturation intensity, which is illustrated in graph 60 of Figure 9. Through experimentation, it was found that this results in almost perfectly linear models under various lighting conditions. It is to be understood, however, that what is linear is not what is illustrated in graph 60, but the difference between the two curves plotted on the horizontal axis.

[0079] Finally, at block 108, with reference to Figure 10, once the image is acquired and the corrector function <p) is applied, the resulting image undergoes dynamic scoping of the region of interest using mathematical thresholding 70. This step is called threshold filling, and it is motivated by the observation that control and test lines on the lateral flow assay (LFA) or lateral flow immunoassay 2 correspond to rectangles, though it is difficult to determine exactly where each rectangle starts and where it ends. This is important information, because it determines which pixels will have an influence on the result and which ones will be excluded. Current commercial solutions for urine-based tests ask users to intervene by drawing a rectangle around the region of interest, as shown in the example of prior art user interface 80 of Figure 11 , but the problem of which pixels to consider and which ones to exclude remains.

[0080] Referring to Figure 2B, there is shown a flow diagram of the temperature and brightness adjustment sub-process 104 in accordance with the illustrative embodiment of the present disclosure. Steps of the sub-process 104 are indicated by blocks 1042 to 1048.

[0081] At block 1042, the color temperature of the captured image from block 102 is estimated. The m x n x 3 captured imagerepresented in the RGB colorspace, is processed to compute the mean value of the red, green, and blue channels across all pixels:

[0082] (Equation 1 )

[0083] This average RGB triplet provides a compact summary of the captured image's overall color tone. To convert this RGB value into a correlated color temperature (CCT) expressed in Kelvin, a colorimetric transformation is applied. This transformation involves mapping the RGB value to an approximate chromaticity coordinate in the CIE 1931 color space and then solving an inverse model to estimate the CCT that would correspond to this chromaticity.

[0084] The average RGB triplet is normalized in the [0,1] range by dividing each value by 255, and then multiplied by the conversion matrix MRGB^xrz:

[0085] (Equation 2)

[0086] The XYZ values are converted to chromaticity (x, y) coordinates:

[0087] x =X+Xy+Z(Equation 3)

[0088] (Equation 4)

[0089] The color temperature estimation may rely on interpolation of empirical data, curve fitting, or computational models of blackbody radiation and standard illuminants to determine the most likely Kelvin temperature that would produce a similar RGB balance under standardized lighting conditions. For example, using McCamy’s approximation:

[0090] (Equation 5)

[0091] CCT(x,y) » 449n3+ 3525n2+ 6823.3n + 5520.33 (Equation 6)

[0092] Then, at block 1044, the captured image’s temperature is adjusted to a reference temperature. The CIE 1931 coordinates calculated at block 1042 are used to obtain equivalent coordinates in the xyY color space:

[0094] The D65 chromaticity coordinates are obtained from standardized tables published by the International Commission on Illumination (CIE) for the given target temperature:

[0095] Txy= [xD65,yD65] (Equation 8)

[0096] These values are used to compute the average shift between the actual (x,y) chromaticity coordinates and the D65 reference:

[0097] Ax = xD65- x (Equation 9)

[0098] Ay = yD65- y (Equation 10)

[0099] These shifts are applied to every pixel of the captured image in the xyY color space. These pixels are converted back to XYZ and then to RGB using the same equations as in in block 1042.

[0100] At block 1046, the brightness of the captured image from block 102 is estimated. The m x n x 3 captured imagerepresented in the RGB color space by computing the luminance-preserved m x n grayscale representation G^-:

[0101] Gi = 0.299 • Ii >0+ 0.587 • 7i+ 0.114 • Ii >2(Equation11 )

[0102] The average brightness is then computed as the mean pixel intensity:

[0103] (Equation 12)

[0104] Then, at block 1048, the captured image’s brightness is adjusted to a reference brightness using a brightness scaling factor computed as:

[0105] (Equation 13)

[0106] This factor is then applied to the RGB captured image (or, optionally, the temperature adjusted image from block 104) pixel-wise, and the values are rounded to the nearest integer between 0 and 255:0, ... , n - 1 (Equation 14)

[0108] In a non-restrictive illustrative embodiment of the present disclosure where the assay result image capturing and analysis system 10 is used for a salivabased application, the control and test lines on the lateral flow assay (LFA) or lateral flow immunoassay 2 are not particularly clear due to lower viscosity. It is important to clearly identify the control and test lines because the predictor metric used by the process 100 is the ratio of the saturation sums of the pixels over both areas.

[0109] A simplified example of the potential impact is illustrated in Figures 12A and 12B, where a line’s pixels have a saturation sum that is equal to 1 + 5 + 2 + 5 = 13 (Figure 12A) and the same line with an extra pixels column to the right whose pixels saturation sum is 13 + 1 + 2 = 16 (Figure 8B). This can result in a 20%+ difference in the numerator or denominator of the ratio. In variousapplications, differences in the order of 10 to 20% range have been observed. It was also observed that this reduces the occurrence of outliers and thus a more robust detection system. This is because threshold filling focuses only on those pixels that are sufficiently intense to estimate the width of a line on the on the lateral flow assay (LFA) or lateral flow immunoassay 2 before using it to build a rectangular region. This increases the signal-to-noise ratio by leaving out the additional noise that would result in being less specific about which pixels to include and exclude from the process’ 100 calculations.

[0110] Experimental trials on a sample of 480 images using four different systems / cameras in three different lighting conditions resulted in a classification precision in the 90%+ range as compared to ±50%, depending on different factors such as how many different systems / cameras were considered, which systems / cameras were used specifically, what lighting conditions were used, etc. Not only does the disclosed system and method increase robustness, they also cause overall analyte concentration classification precision to increase as well. The disclosed assay result image capturing and analysis system 10 does not require additional hardware beyond a smart phone, tablet, laptop or any other such computing device that can be provided with a camera or is capable of acquiring an image, and it provides a single-step procedure for the user (i.e. , taking a reference picture for error correction modeling). The system 10 also has no learning curve and is more robust that what is currently considered the state of the art.

[0111] The various features described herein can be combined in a variety of ways within the context of the present disclosure so as to provide still other embodiments. As such, the embodiments are not mutually exclusive. Moreover, the embodiments discussed herein need not include all of the features and elements illustrated and / or described and thus partial combinations of features can also be contemplated. Furthermore, embodiments with less features than those describedcan also be contemplated. It is to be understood that the present disclosure is not limited in its application to the details of construction and parts illustrated in the accompanying drawings and described hereinabove. The disclosure is capable of other embodiments and of being practiced in various ways. It is also to be understood that the phraseology or terminology used herein is for the purpose of description and not limitation. Hence, although the present disclosure has been provided hereinabove by way of non-restrictive illustrative embodiments thereof, it can be modified, without departing from the scope, spirit and nature thereof and of the appended claims.

Claims

WHAT IS CLAIMED IS:1 . A method for assay result analysis for a given application, the method comprising the steps of: a) acquiring an image of the assay; b) applying saturation intensity matching to the acquired image using a correction function built using a reference color gradient specific to the given application, resulting in a corrected saturation image; c) applying dynamic scoping of a given region of interest of the assay using mathematical thresholding to the corrected saturation image; and d) identifying the presence or absence of pixel rectangles in the given region of interest.

2. A method for assay result analysis according to claim 1 , wherein prior to step b) is performed the step consisting of: aa) adjusting a temperature of the acquired image based on a reference temperature.

3. A method for assay result analysis according to claim 2, wherein step aa) consists of the sub-steps of: i) obtaining a temperature RGB triplet by computing a mean value of red, green and blue channels across all pixels of the acquired image; ii) converting the temperature RGB triplet into a correlated color temperature expressed in Kelvin by applying a colorimetric transformation to the temperature RGB triplet; ill) normalizing the transformed temperature RGB triplet; iv) estimating the color temperature by applying a procedure selected from a group consisting of an interpolation of empirical dataprocedure, a curve fitting procedure, a computational model of blackbody radiation procedure and a standard illuminants procedure to determine the most likely Kelvin temperature that would produce a similar RGB balance to the temperature RGB triplet under standardized lighting conditions; and v) adjusting the color temperature of the acquired image to the reference temperature using standardized tables published by the International Commission on Illumination.

4. A method for assay result analysis according to any one of claims 1 to 3, wherein prior to step b) is performed the step consisting of: ab) adjusting a brightness of the acquired image based on a reference brightness.

5. A method for assay result analysis according to claim 4, wherein step ab) consists of the sub-steps of: i) obtaining a brightness RGB triplet by computing a mean pixel intensity of a luminance-preserved grayscale representation of the acquired image; and ii) adjusting the brightness of the acquired image to the reference brightness using a brightness scaling factor computed as a ratio of the brightness RGB triplet over the reference brightness.

6. A method for assay result analysis according to any one of claims 1 to 5, wherein the image of the assay is acquired from an image capture device, and wherein the saturation intensity matching is further based on a mean absolute error linear regression between a reference saturation level of a given reference color pixel from a reference image capture device and a saturation level of a corresponding color pixel from the image capture device.

7. A method for assay result analysis according to claim 6, wherein the reference saturation level for each given reference color pixel is built from colors observed in images of tests assays for the given application.

8. A method for assay result analysis according to any one of claims 1 to 7, wherein the essay is selected from a group consisting of a lateral flow assay and a lateral flow immunoassay.

9. A system for assay result analysis for a given application, the system comprising: an input interface for acquiring an assay image; at least one processor operatively connected to the input interface, the at least one processor having an associated memory having stored therein processor executable code that when executed by the at least one processor performs the steps of: a) obtain the assay image from the input interface; b) apply saturation intensity matching to the obtained assay image using a correction function built using a reference color gradient specific to the given application, resulting in a corrected saturation image; c) apply dynamic scoping of a given region of interest of the assay using mathematical thresholding to the corrected saturation image; and d) identify the presence or absence of pixel rectangles in the given region of interest.

10. A system for assay result analysis according to claim 9, wherein the image of the assay is acquired from an image capture device via the input interface, and wherein the saturation intensity matching is further based on a mean absoluteerror linear regression between a reference saturation level of a given reference color pixel from a reference image capture device and a saturation level of a corresponding color pixel from the image capture device.

11. A system for assay result analysis according to claim 9, further comprising an image capture device operatively connected to the input interface.

12. A system for assay result analysis according to claim 11 , wherein the saturation intensity matching is based on a mean absolute error linear regression between a reference saturation level of a given reference color pixel from a reference image capture device and a saturation level of a corresponding color pixel from the image capture device.

13. A system for assay result analysis according to either one of claims 10 or 12, wherein the reference saturation level for each given reference color pixel is built from colors observed in images of tests assays for the given application.

14. A system for assay result analysis according to any one of claims 9 to 13, wherein prior to step b) the at least one processor performs the step consisting of: aa) adjusting a temperature of the acquired image based on a reference temperature.

15. A system for assay result analysis according to claim 14, wherein step aa) consists of the sub-steps of: i) obtaining a temperature RGB triplet by computing a mean value of red, green and blue channels across all pixels of the acquired image; ii) converting the temperature RGB triplet into a correlated color temperature expressed in Kelvin by applying a colorimetric transformation to the temperature RGB triplet; ill) normalizing the transformed temperature RGB triplet;iv) estimating the color temperature by applying a procedure selected from a group consisting of an interpolation of empirical data procedure, a curve fitting procedure, a computational model of blackbody radiation procedure and a standard illuminants procedure to determine the most likely Kelvin temperature that would produce a similar RGB balance to the temperature RGB triplet under standardized lighting conditions; and v) adjusting the color temperature of the acquired image to the reference temperature using standardized tables published by the International Commission on Illumination.

16. A system for assay result analysis according to any one of claims 9 to 15, wherein prior to step b) the at least one processor performs the step consisting of: ab) adjusting a brightness of the acquired image based on a reference brightness.

17. A system for assay result analysis according to claim 16, wherein step ab) consists of the sub-steps of: i) obtaining a brightness RGB triplet by computing a mean pixel intensity of a luminance-preserved grayscale representation of the acquired image; and ii) adjusting the brightness of the acquired image to the reference brightness using a brightness scaling factor computed as a ratio of the brightness RGB triplet over the reference brightness.

18. A computing device program product comprising a computing device readable memory storing computing device executable instructions thereon that when executed by a computing device perform the method steps of any one of claims 1 to 8.

Images