CN114894793B - Imaging method, imaging system and server based on artifact elimination - Google Patents
Imaging method, imaging system and server based on artifact elimination Download PDFInfo
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Abstract
The present disclosure describes an imaging method, an imaging system and a server based on artifact cancellation. The imaging method comprises the steps of collecting a fresh sample by using a plane interferometer to obtain a plurality of interferograms to form a first interference image set; performing singular value decomposition on the first interference image set to obtain a time feature matrix and a singular value matrix, wherein each column of the time feature matrix is a time feature vector, calculating the fluctuation degree of the time feature vector, updating singular values in the singular value matrix corresponding to the time feature vector to preset singular values based on the fluctuation degree, and reconstructing the first interference image set based on the updated singular value matrix to serve as a second interference image set; the components of the three-dimensional color space are acquired based on the second set of interference images and the second set of interference images are converted to color images based on the components of the three-dimensional color space, the color images reflecting the internal dynamic signals of the live sample. Thus, an internal dynamic signal of the biological tissue can be obtained in a non-invasive manner.
Description
The present application is a divisional application of an imaging system and an imaging method for signal processing of a planar interferometer, with application number 2021103780927 and application number 2021, 04, 08.
Technical Field
The present disclosure relates generally to an imaging method, an imaging system, and a server based on artifact cancellation.
Background
The internal dynamic signals of biological tissues can provide important references for scientific research and clinical diagnosis in the biomedical field, for example, in the field of histopathology or stem cell tissue engineering, the intensity of the internal dynamic signals of biological tissues is often used for cell type differentiation or cell culture detection. Therefore, how to acquire internal dynamic signals of biological tissues has become a popular research direction in the biomedical field. Internal dynamic signals of biological tissues can currently be acquired by invasive methods (e.g., methods based on fluorescent staining).
However, invasive methods are generally destructive and are not suitable for observation and measurement of living biological tissue, while structural imaging of biological tissue obtained by non-invasive methods (e.g., measurement of biological tissue using a planar interferometer based on the principle of dual beam equal thickness interferometry) has the problem that dynamic signals cannot be reflected and measurement of internal dynamic signals of biological tissue cannot be achieved.
Disclosure of Invention
The present disclosure has been made in view of the above-described circumstances, and an object thereof is to provide an imaging system and an imaging method for signal processing of a planar interferometer capable of obtaining an internal dynamic signal of a biological tissue in a non-invasive manner.
To this end, a first aspect of the present disclosure provides an imaging system for signal processing of a planar interferometer, the planar interferometer performing measurements based on the principle of dual beam equal thickness interferometry, the imaging system comprising: the system comprises an acquisition module, an artifact elimination module, a signal-to-noise ratio enhancement module and a dynamic imaging module; the acquisition module is used for acquiring a plurality of interferograms obtained by continuously acquiring fresh samples by using the plane interferometer, and the interferograms form a first interference image set according to acquisition time; the artifact elimination module is used for carrying out singular value decomposition on the first interference image set to obtain a time feature matrix and a singular value matrix, each column of the time feature matrix is a time feature vector, calculating the fluctuation degree of the time feature vector, screening the time feature vector meeting a threshold value condition based on the fluctuation degree to update singular values in the singular value matrix corresponding to the time feature vector to preset singular values, and reconstructing the first interference image set based on the updated singular value matrix to serve as a second interference image set; the signal-to-noise ratio enhancement module performs signal-to-noise ratio enhancement processing on pixel values in each sliding window in the second interference image set by utilizing sliding windows with preset lengths to obtain a third interference image set, wherein the sliding windows move along the direction of the acquisition time according to preset step sizes; and the dynamic imaging module is used for acquiring a plurality of pixel values of the same positions of a plurality of interferograms in the third interference image set to form a pixel sequence ordered according to the acquisition time, acquiring components of a three-dimensional color space of each position based on time domain information and frequency domain information of the pixel sequence of each position and converting the third interference image set into a color image based on the components of the three-dimensional color space, wherein the color image reflects an internal dynamic signal of the fresh sample, the components of the three-dimensional color space comprise a hue component, a saturation component and a brightness component, and acquiring a target frequency based on the frequency domain information, and the hue component corresponds to the target frequency. In the present disclosure, an imaging system obtains a time feature vector based on singular value decomposition to eliminate artifacts of a plurality of interferograms by a fluctuation degree of the time feature vector and to perform signal-to-noise enhancement processing on the plurality of interferograms by using a sliding window, and finally generates a color image based on time domain information and frequency domain information of the plurality of interferograms. Thus, the influence of artifacts or noise on the image quality of the color image can be reduced, further research based on the color image is facilitated, and in addition, the color image generated by the present disclosure can reflect the internal dynamic signal of biological tissue and the manner in which the color image is obtained is non-invasive.
In addition, in the imaging system related to the first aspect of the present disclosure, optionally, the imaging system further includes a calibration module, where the calibration module is configured to establish a linear correspondence between the target frequency and the color so as to calibrate the correspondence between the target frequency and the color. This can intuitively obtain the frequency corresponding to the color in the color image.
In addition, in the imaging system according to the first aspect of the present disclosure, optionally, the artifact removal module uses a cumulative zero-crossing rate of the temporal feature vector to represent a fluctuation degree of the temporal feature vector, and the cumulative zero-crossing rate of the i-th column temporal feature vector is represented as: D_ZRC i ＝|ZRC i+1 -ZRC i I, wherein i is the column index of the temporal feature matrix, ZRC i The zero crossing rate of the ith column time feature vector. Thereby, accumulation can be utilizedThe zero crossing represents the degree of fluctuation of the temporal feature vector.
Further, in the imaging system according to the first aspect of the present disclosure, optionally, the threshold condition is that a cumulative zero-crossing rate of each temporal feature vector is greater than a preset value, wherein the preset value is a standard deviation of the cumulative zero-crossing rate of 3 times. Thereby, it is possible to eliminate the artifact by eliminating the signal that the degree of dispersion of the cumulative zero-crossing rate reaches the preset value degree.
Further, in the imaging system according to the first aspect of the present disclosure, optionally, the preset length is greater than 1, and a dimension of the sliding window coincides with a dimension of the second interference image set.
Further, in the imaging system according to the first aspect of the present disclosure, optionally, in the signal-to-noise ratio enhancement process, an average value of the pixel values in each sliding window is obtained, a difference between the pixel value in each sliding window and the average value is summed to obtain a cumulative value, an absolute value of the cumulative value is added to the preset length to obtain an average cumulative value, and the average cumulative value is taken as the pixel value of the third interference image set. Thus, the signal-to-noise ratio enhancement processing can be performed on the second interference image set based on the sliding window and the accumulated algorithm.
Additionally, in the imaging system related to the first aspect of the present disclosure, optionally, the dynamic imaging module acquires a power spectral density based on a pixel sequence of each position and takes an inverse of a bandwidth of the power spectral density as the saturation component; the dynamic imaging module performs Fourier transform on the pixel sequences at all positions to obtain a frequency sequence, and obtains the target frequency based on the frequency sequence and the power spectral density and serves as the tone component; the dynamic imaging module obtains a standard deviation or variance of a pixel sequence based on the pixel sequence of each position and takes the standard deviation or variance as the brightness component. Thereby, a saturation component, a hue component, and a luminance component can be obtained based on the pixel sequence of each position.
A second aspect of the present disclosure provides an imaging method for signal processing of a planar interferometer that performs measurement based on a dual-beam equal-thickness interference principle, the imaging method comprising: acquiring a plurality of interferograms obtained by continuously acquiring a fresh sample by using the plane interferometer, wherein the interferograms form a first interference image set according to acquisition time; performing singular value decomposition on the first interference image set to obtain a time feature matrix and a singular value matrix, wherein each column of the time feature matrix is a time feature vector, calculating the fluctuation degree of the time feature vector, screening the time feature vector meeting a threshold value condition based on the fluctuation degree to update the singular value in the singular value matrix corresponding to the time feature vector to a preset singular value, and reconstructing the first interference image set based on the updated singular value matrix to serve as a second interference image set; performing signal-to-noise ratio enhancement processing on pixel values in each sliding window in the second interference image set by utilizing sliding windows with preset lengths to obtain a third interference image set, wherein the sliding windows move along the direction of the acquisition time according to preset step sizes; and acquiring a plurality of pixel values of the same positions of the plurality of interferograms in the third interference image set to form a pixel sequence ordered according to the acquisition time, acquiring components of a three-dimensional color space of each position based on time domain information and frequency domain information of the pixel sequence of each position, and converting the third interference image set into a color image based on the components of the three-dimensional color space, wherein the color image reflects an internal dynamic signal of the live sample, the components of the three-dimensional color space include a hue component, a saturation component and a brightness component, and acquiring a target frequency based on the frequency domain information, the hue component corresponding to the target frequency. In the method, a time feature vector is obtained based on singular value decomposition, so that artifacts of a plurality of interferograms are eliminated through fluctuation degrees of the time feature vector, signal-to-noise ratio enhancement processing is performed on the plurality of interferograms by utilizing a sliding window, and finally a color image is generated based on time domain information and frequency domain information of the plurality of interferograms. Thus, the influence of artifacts or noise on the image quality of the color image can be reduced, further research based on the color image is facilitated, and in addition, the color image generated by the present disclosure can reflect the internal dynamic signal of biological tissue and the manner in which the color image is obtained is non-invasive.
In addition, in the imaging method according to the second aspect of the present disclosure, optionally, a linear correspondence relationship between the target frequency and the color is established to calibrate the correspondence relationship between the target frequency and the color. This can intuitively obtain the frequency corresponding to the color in the color image.
In addition, in the imaging method according to the second aspect of the present disclosure, optionally, the degree of fluctuation of the temporal feature vector is represented by a cumulative zero-crossing rate of the temporal feature vector, and the cumulative zero-crossing rate of the temporal feature vector of the ith column is represented as: D_ZRC i ＝|ZRC i+1 -ZRC i I, wherein i is the column index of the temporal feature matrix, ZRC i Zero crossing rate of the ith column time feature vector; the threshold condition is that the cumulative zero-crossing rate of each time feature vector is larger than a preset value, wherein the preset value is 3 times the standard deviation of the cumulative zero-crossing rate. Thereby, it is possible to eliminate the artifact by eliminating the signal that the degree of dispersion of the cumulative zero-crossing rate reaches the preset value degree.
According to the present disclosure, an imaging system and an imaging method for signal processing of a planar interferometer that obtains an internal dynamic signal of biological tissue in a non-invasive manner can be provided.
Drawings
The present disclosure will now be explained in further detail by way of example only with reference to the accompanying drawings, in which:
fig. 1 is an application scene diagram illustrating an imaging method for signal processing of a planar interferometer according to an example of the present disclosure.
Fig. 2 is a block diagram illustrating an imaging system for signal processing for a planar interferometer in accordance with examples of the present disclosure.
Fig. 3 is a schematic diagram illustrating an interferogram according to an example of the present disclosure.
Fig. 4 (a) is a schematic diagram showing a color image generated for the case where the artifact is not eliminated, to which the example of the present disclosure relates.
Fig. 4 (b) is a schematic diagram showing a color image generated for the case of eliminating artifacts, which is related to an example of the present disclosure.
Fig. 4 (c) is a schematic diagram illustrating the cancellation of artifacts involved in examples of the present disclosure.
Fig. 5 is a schematic diagram illustrating a sliding window movement in accordance with examples of the present disclosure.
Fig. 6 is a schematic diagram illustrating a color image with calibration information according to an example of the present disclosure.
Fig. 7 is a flowchart illustrating an imaging method for signal processing of a planar interferometer according to an example of the present disclosure.
Detailed Description
Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the following description, the same members are denoted by the same reference numerals, and overlapping description thereof is omitted. In addition, the drawings are schematic, and the ratio of the sizes of the components to each other, the shapes of the components, and the like may be different from actual ones. It should be noted that the terms "comprises" and "comprising," and any variations thereof, in this disclosure, such as a process, method, system, article, or apparatus that comprises or has a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include or have other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. All methods described in this disclosure can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context.
The imaging system and imaging method for signal processing of a planar interferometer according to the present disclosure can obtain a color image reflecting an internal dynamic signal of biological tissue in a non-invasive manner based on a plurality of interferograms acquired by the planar interferometer. The imaging method to which the present disclosure relates is applied to an imaging system (described later). The present disclosure is described in detail below with reference to the accompanying drawings. In addition, the application scenario described in the examples of the present disclosure is for more clearly explaining the technical solution of the present disclosure, and does not constitute a limitation on the technical solution provided by the present disclosure.
Fig. 1 is an application scene diagram illustrating an imaging method for signal processing of a planar interferometer according to an example of the present disclosure. As shown in fig. 1, biological tissue 120 may be measured using a planar interferometer 110 to obtain a multi Zhang Ganshe map 130. After the acquisition of the plurality of interferograms 130 is completed, the plurality of interferograms 130 may be submitted to the server 140, and the server 140 may implement an imaging method by which the plurality of interferograms 130 are received and a color image 150 is generated by executing computer program instructions stored on the server 140.
In some examples, the planar interferometer 110 can make measurements based on the principle of dual beam equal thickness interferometry. In some examples, the planar interferometer 110 may be a two-dimensional planar interferometer, which may be the acquisition of the interferogram 130 by a 2D camera. In some examples, the device that obtains the plurality of interferograms 130 may be any device that performs measurements based on the dual beam equal thickness interference principle. In some examples, biological tissue 120 may be a cellular architecture between cells and organs.
In some examples, server 140 may include one or more processors and one or more memories. The processor may include, among other things, a central processing unit, a graphics processing unit, and any other electronic components capable of processing data, capable of executing computer program instructions. The memory may be used to store computer program instructions. In some examples, server 140 may implement the imaging method by executing computer program instructions on a memory. In some examples, server 140 may also be a cloud server.
The imaging system 1 to which the present disclosure relates is described in detail below with reference to the accompanying drawings. The imaging system 1 to which the present disclosure relates is for implementing the above-described imaging method. Fig. 2 is a block diagram illustrating an imaging system 1 for signal processing of a planar interferometer according to an example of the present disclosure. Fig. 3 is a schematic diagram illustrating an interferogram according to an example of the present disclosure.
As shown in fig. 2, in some examples, the imaging system 1 may include an acquisition module 10, and the acquisition module 10 may be configured to acquire a plurality of interferograms and to form a first set of interference images. The interferogram may be a pattern formed by the interference of waves. In some examples, the dimensions of the multiple interferograms may be uniform. For example, the multiple interferograms may each be 1440 x 1440 or 1024 x 1024 in size. Examples of the present disclosure are not limited thereto and in other examples, the size of the interferogram may be determined by a camera used to acquire the interferogram in a planar interferometer. In some examples, the number of the plurality of interferograms may be greater than or equal to a preset number. The predetermined number may be, for example, 16, and the number of the plurality of interferograms may be, for example, 16, 32, 48, 64, 100, 512, 1024, or the like. As an example of an interferogram, fig. 3 shows a schematic diagram of an interferogram obtained by planar interferometer measurements.
Additionally, in some examples, a sample may be acquired using a planar interferometer to obtain multiple interferograms. In some examples, the sample may be a fresh sample. Such as a live sample of mouse retina. In some examples, the fresh sample may include contents. In some examples, the content may be cells. In some examples, the plurality of interferograms may be obtained by sequential acquisition. In some examples, the continuous acquisition may be based on a selected exposure frequency (i.e., number of exposures per second). For example, the exposure frequency may be selected to be 100 consecutive acquisitions per second to obtain 512 interferograms, in which case the acquisition of 512 interferograms takes 5.12 seconds to complete.
Additionally, in some examples, the plurality of interferograms may be organized into a first set of interference images in terms of acquisition time. Therefore, a plurality of interference patterns can be conveniently and simultaneously processed.
Fig. 4 (a) is a schematic diagram showing a color image generated for the case where the artifact is not eliminated, to which the example of the present disclosure relates. Fig. 4 (b) is a schematic diagram showing a color image generated for the case of eliminating artifacts, which is related to an example of the present disclosure. Fig. 4 (c) is a schematic diagram illustrating the cancellation of artifacts involved in examples of the present disclosure.
As shown in fig. 2, in some examples, the imaging system 1 may include a cancellation artifact module 11, and the cancellation artifact module 11 may be configured to perform a cancellation artifact process on the first set of interference images to obtain the second set of interference images. In general, artifacts (Artifacts) refer to various forms of images that appear on the final imaged image, in the absence of an otherwise scanned object, such as a live sample. In general, there are often a variety of causes of artifacts. For example, live samples may have motion in an active state that may cause artifacts, which motion is very small at a spatial level, e.g., on the order of microns. Also, for example, disturbances in the ambient air may cause vibrations and thus artifacts. In this case, removing artifacts in the first interference image set can improve the image quality of the subsequently generated color image.
As a comparative example of color images generated without removing artifacts and removing artifacts. Fig. 4 (a) and 4 (b) show a color image generated in the case of non-artifact removal and a color image generated in the case of artifact removal, respectively, of a live sample of a mouse retina, and fig. 4 (c) shows artifact removal in the live sample of a mouse retina. As can be seen from fig. 4 (a), 4 (B) and 4 (C), the contrast of the region C from which the artifact shown in the region B is removed is significantly enhanced relative to the region a from which the artifact is not removed. Thereby, small cells occluded by the artifact can be identified. Here, the region a, the region B, and the region C correspond to the same position in the generated color image.
In some examples, the cancellation artifact module 11 may perform a cancellation artifact process on the first set of interference images based on the singular value decomposition to obtain the second set of interference images. In some examples, in the artifact removal process, a singular value decomposition (Singular Value Decomposition, SVD) may be performed on the first set of interference images to obtain a temporal feature matrix and a singular value matrix.
Specifically, the first interference image set may be converted into two-dimensional data S, which may have a size of m×n, where m may be a resolution (size) of one interference pattern, and n may be a number of multiple interference patterns. In some examples, each row in the two-dimensional data S may include all pixel values of the respective interferograms. For example, 512 Zhang Ganshe images are acquired sequentially, each having dimensions 1440×1440, where m may be 1440×1440 and n may be 512.
In some examples, the two-dimensional data S is subjected to singular value decomposition to represent the two-dimensional data S as:
S＝U∑V，
wherein U is a space feature matrix, sigma is a singular value matrix, and V is a time feature matrix. In some examples, the temporal feature matrix may be n×n in size. For example, assuming that 512 Zhang Ganshe maps are acquired consecutively, the size of the temporal feature matrix may be 512×512. The matrix of singular values may be a diagonal matrix, with the elements on the diagonal being one singular value. In some examples, the size of the singular value matrix may take n×n. For example, assuming a 512 Zhang Ganshe graph is continuously acquired, the size of the singular value matrix may be 512 x 512.
In some examples, each column of the temporal feature matrix may be taken as a temporal feature vector after the temporal feature matrix and the singular value matrix are acquired. Because the time feature vector corresponding to the artifact is greatly different from the time feature vector corresponding to the normal interference signal, the artifact can be distinguished based on the fluctuation degree of the time feature vector. In some examples, the singular values in the singular value matrix may be updated based on the degree of fluctuation of the temporal feature vector. Specifically, the fluctuation degree of the time feature vector can be calculated, the time feature vector meeting the threshold value condition is screened based on the fluctuation degree, and then the singular value in the singular value matrix corresponding to the time feature vector is updated to be the preset singular value. Thus, the singular values in the singular value matrix can be updated based on the degree of fluctuation of the temporal feature vector. In some examples, the preset singular value may be 0.
In some examples, the degree of fluctuation may be represented by a cumulative zero-crossing rate of the temporal feature vector. That is, the cancellation artifact module 11 may represent the degree of fluctuation of the temporal feature vector using the cumulative zero crossing rate of the temporal feature vector. As described above, each column of the temporal feature matrix may be regarded as a temporal feature vector. In some examples, the cumulative zero-crossing rate of the ith column temporal feature vector may be expressed as:
D_ZRC i ＝|ZRC i+1 -ZRC i |，
Where i is the column index of the time feature matrix, ZRC i The zero crossing rate of the ith column time feature vector. The zero crossing rate may refer to the rate of sign change of a signal, and in this disclosure may refer to the number of times a ripple plot (also referred to as a change plot) formed by a plurality of values in a temporal feature vector passes through zero. Thereby, the degree of fluctuation of the time feature vector can be represented by the cumulative zero crossing. In some examples, a curve for each temporal feature vector may be plotted and zero-crossing rate calculated based on the curve. In some examples, i may take on values from 1 to n-1, n may be the number of interferograms.
Examples of the present disclosure are not limited thereto and in other examples, the degree of fluctuation may be represented in other ways.
In some examples, the threshold condition may be that the cumulative zero-crossing rate of the respective temporal feature vector is greater than a preset value. In some examples, the preset value may be a standard deviation of 3 times the cumulative zero-crossing rate. That is, the threshold condition may be expressed as:
D_ZRC i >3×std(D_ZRC)，
wherein D is _ ZRC i For the cumulative zero-crossing rate of the ith column temporal feature vector, D _ ZRC is the set of cumulative zero-crossing rates. Thereby, it is possible to eliminate the artifact by eliminating the signal that the degree of dispersion of the cumulative zero-crossing rate reaches the preset value degree. Examples of the present disclosure are not limited thereto, and in other examples, the preset value may be set according to experimental experience or experimental effect.
As described above, the singular values in the singular value matrix may be updated based on the degree of fluctuation of the temporal feature vector. In some examples, the first set of interference images may be reconstructed based on the updated singular value matrix and as the second set of interference images. Thereby, the artifact removal processing can be performed on the first interference image set based on the degree of fluctuation of the temporal feature vector. Specifically, the spatial feature matrix, the updated singular value matrix, and the temporal feature matrix may be multiplied to obtain updated two-dimensional data, and then a second interference image set corresponding to the first interference image set may be obtained based on the updated two-dimensional data.
However, examples of the present disclosure are not limited thereto, and in other examples, the imaging system 1 may not include the artifact removal module 11. In this case, a subsequent module, such as the signal-to-noise enhancement module 12 or the dynamic imaging module 13, may process the first set of interference images.
Fig. 5 is a schematic diagram illustrating a sliding window movement in accordance with examples of the present disclosure. As shown in fig. 2, in some examples, the imaging system 1 may include a signal-to-noise ratio enhancement module 12, and the signal-to-noise ratio enhancement module 12 may be configured to perform a signal-to-noise ratio enhancement process on the second set of interference images to obtain a third set of interference images. In some examples, the signal-to-noise enhancement module 12 may perform a signal-to-noise enhancement process on pixel values in each sliding window in the second set of interference images using sliding windows of a preset length to obtain the third set of interference images. In some examples, the preset length may be greater than 1. In some examples, the preset length may be 8.
In some examples, the second set of interference images may be represented as a matrix of three dimensions, where the first and second dimensions may correspond to the dimensions of the interference pattern and the third dimension corresponds to the acquisition order (which may also be referred to as acquisition time). In some examples, the dimensions of the sliding window may be consistent with the dimensions of the second set of interference images. In some examples, the first and second dimensions of the sliding window may be consistent with the dimensions of the interferogram. In this case, the sliding window can be moved along the acquisition sequence based on the size of the interferograms.
In some examples, the sliding window may be moved in the direction of the acquisition time. As an example of the sliding window movement, fig. 5 is a schematic diagram showing that the sliding window H of a preset length of 2 moves along the second interference image set L, wherein the second interference image set L contains a plurality of interference patterns, for example, the number of the plurality of interference patterns may be n, and the plurality of interference patterns may include p1, p2, p3, … …, pn, and the like.
In some examples, the sliding window may be moved in the direction of the acquisition time in a preset step size. In some examples, the preset step size may be a preset proportion of the preset length. In some examples, the preset ratio may be less than 1. For example, the predetermined ratio may be 25%, 50%, 75%, 1/3, 2/3, or the like. Specifically, assuming that the preset ratio is 50%, the number of interferograms in the second interference image set is 512, the preset length is 8, the preset step length is 4, and the number of sliding windows is 512/4, that is, 128. In this case, there is an overlap between the sliding windows, and the latter sliding window can be subjected to signal-to-noise enhancement processing in combination with a part of the interferograms in the former sliding window. However, examples of the present disclosure are not limited thereto, and in other examples, the preset step size and the preset length may be set according to actual situations, for example, the preset length may be 6 and the preset step size may be 2.
In some examples, in the signal-to-noise ratio enhancement process, an average of pixel values in each sliding window may be obtained. In some examples, differences between pixel values and average values in respective sliding windows may be summed to obtain a cumulative value. In some examples, the absolute value of the cumulative value may be divided by the preset length to obtain an average cumulative value, which is taken as the pixel value of the third interference image set. Thus, the signal-to-noise ratio enhancement processing can be performed on the second interference image set based on the sliding window and the accumulated algorithm.
Specifically, a third interference image set S 3 Can be expressed as:
where λ is a preset length (also referred to as sliding window length), W k For pixel values within the kth sliding window,the CumSum is the cumulative sum function, and K is the number of sliding windows, which is the average of the pixel values in the kth sliding window.
However, examples of the present disclosure are not limited thereto, and in other examples, the imaging system 1 may not include the signal-to-noise ratio enhancement module 12. In this case, a subsequent module, such as the dynamic imaging module 13, may process the first set of interference images or the second set of interference images.
As shown in fig. 2, in some examples, the imaging system 1 may include a dynamic imaging module 13, and the dynamic imaging module 13 may generate a color image based on the third set of interference images. The color image may reflect the internal dynamic signal of the live sample. In some examples, components of the three-dimensional color space may be acquired based on the third set of interference images and the third set of interference images may be converted to color images based on the components of the three-dimensional color space. In some examples, the components of the three-dimensional color space may include a hue component, a saturation component, and a brightness component (which may also be referred to as a brightness component). In some examples, the three-dimensional color space may be an HSV (Hue, saturation, value) space or an HSI (Hue, saturation, brightness) space. Thereby, a color image can be generated by HSV space or HSI space. In some examples, the color image may be an image of RGB (Red: red, green: green, blue: blue) space.
In some examples, a plurality of pixel values for the same location of the plurality of interferograms in the third set of interference images may be acquired to form a sequence of pixels ordered by acquisition time, components of a three-dimensional color space for each location are acquired based on time domain information and frequency domain information for the sequence of pixels for each location, and the third set of interference images is converted to a color image based on the components of the three-dimensional color space.
In general, the frequency domain information may include frequency components of the signal and magnitudes of the respective frequency components, the time domain information may include magnitude information of the signal, and a time domain signal waveform may be measured as a change in time. As described above, the pixel sequence is formed by ordering the plurality of pixel values at the respective positions according to the acquisition time, and thus, in the present disclosure, the temporal information may refer to the pixel values in the pixel sequence that vary with the acquisition time. In some examples, the signal may be transformed from the time domain to the frequency domain by a fourier series or fourier transform to obtain frequency domain information.
As described above, the components of the three-dimensional color space may include a hue component, a saturation component, and a brightness component. In some examples, dynamic imaging module 13 may obtain tonal components based on frequency domain information. In some examples, dynamic imaging module 13 may obtain a target frequency based on the frequency domain information and a tonal component based on the target frequency.
In some examples, the tonal component may correspond to a target frequency. In some examples, the target frequency may be taken as the tonal component. In some examples, the target frequency may be an average frequency or a median frequency. In some examples, the dynamic imaging module 13 may fourier transform the pixel sequences of the respective positions to acquire a frequency sequence (i.e., frequency domain information), acquire an average frequency based on the frequency sequence and a power spectral density (described later), and as a tone component. In some examples, the frequency sequence and the power spectral density may be dot-product to obtain an average frequency. Thereby, tone components can be obtained based on the pixel sequences of the respective positions.
As described above, the target frequency may be a median frequency. In some examples, the median frequency may be a frequency that divides the power spectral density into upper and lower equal power areas. In some examples, the median frequency MDF may satisfy the formula:
wherein P (f) is the power spectral density and f is the frequency.
In some examples, dynamic imaging module 13 may obtain the saturation component based on a sequence of pixels (also instant domain information) for each location. In some examples, dynamic imaging module 13 may obtain power spectral densities based on a sequence of pixels at each location and saturation components based on the power spectral densities. In some examples, the power spectral density may be normalized and the saturation component may be obtained based on the normalized power spectral density. In some examples, dynamic imaging module 13 may obtain power spectral densities based on a sequence of pixels at each location and take the inverse of the bandwidth of the power spectral densities as the saturation component. In some examples, the power spectral density may be obtained based on a sequence of pixels at each location and using the Welch method. Thereby, the saturation component can be obtained based on the pixel sequence of each position.
In some examples, dynamic imaging module 13 may obtain a standard deviation or variance of a pixel sequence (also time domain information) for each location based on the pixel sequence and take the standard deviation or variance as a luminance component. Thereby, the luminance component can be obtained based on the pixel sequence of each position.
In some examples, the dynamic imaging module 13 may normalize the plurality of interferograms in the third set of interference images before acquiring the pixel sequences for each location and acquire the pixel sequences for each location based on the normalized interferograms. Thus, a plurality of interferograms can be normalized. In some examples, the normalization method may include, but is not limited to, L1 normalization or L2 normalization.
Fig. 6 is a schematic diagram showing a color image with calibration information D according to an example of the present disclosure. In some examples, the imaging system 1 may further include a calibration module (not shown) that may be used to establish a linear correspondence of the target frequency to the color to calibrate the correspondence of the target frequency to the color. In some examples, the calibration module may be configured to obtain a frequency range of the target frequency, and calibrate a correspondence between the target frequency and the color based on a linear correspondence between the frequency range and a preset color range. This can intuitively obtain the frequency corresponding to the color in the color image. In some examples, the linear correspondence may be expressed as:
Wherein, [ H ] min ，H max ]For frequency range, H in For the frequency of input, H min At the minimum target frequency, H max For maximum target frequency, C out For the color of the output, [ C ] min ，C max ]For a preset color range C min Is the minimum value in the preset color range, C max Is the maximum value in the preset color range. In some examples, [ C min ，C max ]May be [0,4/3 pi ]]. In this case C min Is 0, C max Is 4/3 pi. Thus, a better visual effect can be obtained. Examples of the present disclosure are not limited thereto, in other examples, [ C min ，C max ]May be [0,2 pi ]]. As an example of calibration, fig. 6 shows a color image of a live sample of the murine retina with calibration information D. As shown in fig. 6, the calibration information D may be a color scale. In this case, the frequency corresponding to the color in the color image can be intuitively obtained.
The imaging system 1 of the present disclosure obtains a time feature vector based on singular value decomposition to eliminate artifacts of a plurality of interferograms by a fluctuation degree of the time feature vector and performs signal-to-noise enhancement processing on the plurality of interferograms by using a sliding window, and finally generates a color image based on time domain information and frequency domain information of the plurality of interferograms. Thus, the influence of artifacts or noise on the image quality of the color image can be reduced, further research based on the color image is facilitated, and in addition, the color image generated by the present disclosure can reflect the internal dynamic signal of biological tissue and the manner in which the color image is obtained is non-invasive.
The imaging method of the present disclosure for signal processing of a planar interferometer is described in detail below in conjunction with fig. 7. The imaging method of signal processing for a planar interferometer to which the present disclosure relates may sometimes be simply referred to as an imaging method. The imaging method is applied to the imaging system 1 described above. Fig. 7 is a flowchart illustrating an imaging method for signal processing of a planar interferometer according to an example of the present disclosure.
In some examples, as shown in fig. 7, the imaging method may include acquiring a plurality of interferograms and forming a first set of interference images (step S110), and in step S110, a sample may be acquired using a planar interferometer to obtain the plurality of interferograms. In some examples, the sample may be a fresh sample. In some examples, the plurality of interferograms may be obtained by sequential acquisition. In some examples, the plurality of interferograms may be grouped into a first set of interference images in terms of acquisition time. Therefore, a plurality of interference patterns can be conveniently and simultaneously processed. For a detailed description, reference may be made to the relevant description of the acquisition module 10.
In some examples, as shown in fig. 7, the imaging method may include performing an artifact removal process on the first set of interference images to obtain a second set of interference images (step S120). In this case, removing artifacts in the first interference image set can improve the image quality of the subsequently generated color image. In some examples, in the artifact removal process of step S120, a singular value decomposition may be performed on the first set of interference images to obtain a temporal feature matrix and a singular value matrix. In some examples, each column of the temporal feature matrix may be taken as a temporal feature vector after the temporal feature matrix and the singular value matrix are acquired. In some examples, the singular values in the singular value matrix may be updated based on the degree of fluctuation of the temporal feature vector. Specifically, the fluctuation degree of the time feature vector can be calculated, the time feature vector meeting the threshold value condition is screened based on the fluctuation degree, and then the singular value in the singular value matrix corresponding to the time feature vector is updated to be the preset singular value. Thus, the singular values in the singular value matrix can be updated based on the degree of fluctuation of the temporal feature vector. In some examples, the preset singular value may be 0. For a detailed description, reference may be made to the relevant description of the artifact removal module 11.
In some examples, in step S120, the degree of fluctuation may be represented by the cumulative zero-crossing rate of the temporal feature vector. That is, the degree of fluctuation of the temporal feature vector can be expressed by the cumulative zero crossing rate of the temporal feature vector. As described above, each column of the temporal feature matrix may be regarded as a temporal feature vector. In some examples, the cumulative zero-crossing rate of the ith column temporal feature vector may be expressed as: D_ZRC i ＝|ZRC i+1 -ZRC i I, where i is the column index of the temporal feature matrix, ZRC i The zero crossing rate of the ith column time feature vector. Thereby, the degree of fluctuation of the time feature vector can be represented by the cumulative zero crossing. In some examples, the threshold condition may be that the cumulative zero-crossing rate of the respective temporal feature vector is greater than a preset value. In some examples, the preset value may be a standard deviation of 3 times the cumulative zero-crossing rate. Thereby, it is possible to eliminate the artifact by eliminating the signal that the degree of dispersion of the cumulative zero-crossing rate reaches the preset value degree. For a detailed description, reference may be made to the relevant description in the artifact removal module 11 in the imaging system 1 regarding the cumulative zero-crossing rate. In some examples, in step S120, the first set of interference images may be reconstructed based on the updated singular value matrix and as the second set of interference images. For a detailed description, reference may be made to the relevant description in the artifact removal module 11 regarding reconstructing the first set of interference images.
In some examples, as shown in fig. 7, the imaging method may include performing a signal-to-noise enhancement process on the second set of interference images to obtain a third set of interference images (step S130). In step S130, a signal-to-noise ratio enhancement process may be performed on the pixel values in each sliding window in the second interference image set using a sliding window of a preset length to obtain a third interference image set. In some examples, the preset length may be greater than 1. In some examples, the preset length may be 8. In some examples, the dimensions of the sliding window may be consistent with the dimensions of the second set of interference images. In some examples, the sliding window may be moved in the direction of the acquisition time in a preset step size. In some examples, the preset step size may be a preset proportion of the preset length. For example, the predetermined ratio may be 25%, 50%, 75%, 1/3, 2/3, or the like. In some examples, in the signal-to-noise ratio enhancement process, an average of pixel values in each sliding window may be obtained. In some examples, differences between pixel values and average values in respective sliding windows may be summed to obtain a cumulative value. In some examples, the absolute value of the cumulative value may be divided by the preset length to obtain an average cumulative value, which is taken as the pixel value of the third interference image set. Thus, the signal-to-noise ratio enhancement processing can be performed on the second interference image set based on the sliding window and the accumulated algorithm. For a detailed description, reference may be made to the relevant description of the signal-to-noise enhancement module 12.
In some examples, as shown in fig. 7, the imaging method may include generating a color image based on the third set of interference images (step S140). The color image may reflect the internal dynamic signal of the live sample. In some examples, components of the three-dimensional color space may be acquired based on the third set of interference images and the third set of interference images may be converted to color images based on the components of the three-dimensional color space. In some examples, the components of the three-dimensional color space may include a hue component, a saturation component, and a brightness component (which may also be referred to as a brightness component). In some examples, a plurality of pixel values for the same location of the plurality of interferograms in the third set of interference images may be acquired to form a sequence of pixels ordered by acquisition time, components of a three-dimensional color space for each location are acquired based on time domain information and frequency domain information for the sequence of pixels for each location, and the third set of interference images is converted to a color image based on the components of the three-dimensional color space. For a specific description reference may be made to the relevant description of the dynamic imaging module 13.
In some examples, in step S140, the tonal component may correspond to a target frequency. In some examples, the target frequency may be taken as the tonal component. In some examples, the target frequency may be an average frequency or a median frequency. In some examples, the sequence of pixels at each location may be fourier transformed to obtain a sequence of frequencies, and an average frequency is obtained as a tonal component based on the sequence of frequencies and the power spectral density. In some examples, in step S140, the power spectral density may be obtained based on the pixel sequence of each location and the inverse of the bandwidth of the power spectral density may be taken as the saturation component. In some examples, the power spectral density may be obtained based on a sequence of pixels at each location and using the Welch method. In some examples, in step S140, a standard deviation or variance of a pixel sequence of each position may be acquired based on the pixel sequence and taken as a luminance component. For a detailed description, reference may be made to the relevant description in the dynamic imaging module 13 regarding hue components, saturation components and brightness components.
However, examples of the present disclosure are not limited thereto, and in other examples, the imaging method may not include step S120 and/or step S130. That is, the plurality of interferograms used to generate the color image may not undergo an artifact removal process and/or a signal-to-noise enhancement process.
In some examples, the imaging method further includes establishing a linear correspondence of the target frequency to the color to calibrate the correspondence of the target frequency to the color (not shown). In some examples, the method may be used to obtain a frequency range of the target frequency, and calibrate a correspondence between the target frequency and the color based on a linear correspondence between the frequency range and a preset color range. This can intuitively obtain the frequency corresponding to the color in the color image. For a specific description, reference may be made to the relevant description of the calibration module.
According to the imaging method, a time feature vector is obtained based on singular value decomposition, so that artifacts of a plurality of interferograms are eliminated through fluctuation degrees of the time feature vector, a sliding window is utilized to conduct signal-to-noise ratio enhancement processing on the plurality of interferograms, and finally a color image is generated based on time domain information and frequency domain information of the plurality of interferograms. Thus, the influence of artifacts or noise on the image quality of the color image can be reduced, further research based on the color image is facilitated, and in addition, the color image generated by the present disclosure can reflect the internal dynamic signal of biological tissue and the manner in which the color image is obtained is non-invasive.
While the invention has been described in detail in connection with the drawings and embodiments, it should be understood that the foregoing description is not intended to limit the invention in any way. Modifications and variations of the invention may be made as desired by those skilled in the art without departing from the true spirit and scope of the invention, and such modifications and variations fall within the scope of the invention.
Claims (8)
1. An imaging method based on artifact cancellation, comprising: continuously acquiring a fresh sample by using a plane interferometer to acquire a plurality of interferograms and forming a first interference image set according to acquisition time, wherein the plane interferometer is used for measuring based on a double-beam equal-thickness interference principle; performing an artifact removal process on the first interference image set to obtain a second interference image set, in the artifact removal process, performing singular value decomposition on the first interference image set to obtain a time feature matrix and a singular value matrix, each column of the time feature matrix being a time feature vector, calculating a fluctuation degree of the time feature vector and screening time feature vectors meeting a threshold condition based on the fluctuation degree to obtain the time feature vector The singular values in the singular value matrix corresponding to the vectors are updated to preset singular values, then the first interference image set is reconstructed based on the updated singular value matrix and used as the second interference image set, the cumulative zero-crossing rate of the time eigenvectors is used for representing the fluctuation degree of the time eigenvectors, and the cumulative zero-crossing rate of the time eigenvectors in the ith column is represented as follows: D_ZRC i ＝|ZRC i+1 -ZRC i I, wherein i is the column index of the temporal feature matrix, ZRC i Zero crossing rate of the ith column time feature vector; performing signal-to-noise ratio enhancement processing on pixel values in each sliding window in the second interference image set by utilizing a sliding window with a preset length to obtain a third interference image set, wherein the sliding window moves along the direction of the acquisition time according to a preset step length, the second interference image set is expressed as a three-dimensional matrix, the first dimension and the second dimension in the three dimensions correspond to the dimension of the interference image, the third dimension in the three dimensions corresponds to the acquisition time, and the dimension of the sliding window is consistent with the dimension of the second interference image set; acquiring components of a three-dimensional color space based on the third set of interference images; and converting the third interference image set into a color image based on the components of the three-dimensional color space, the color image reflecting an internal dynamic signal of the live sample and obtaining the color image in a non-invasive manner, in obtaining the components of the three-dimensional color space based on the third interference image set, obtaining a plurality of pixel values of the same positions of a plurality of interferograms in the third interference image set to form a pixel sequence ordered in the acquisition time, obtaining the components of the three-dimensional color space of each position based on time domain information and frequency domain information of the pixel sequence of each position, the components of the three-dimensional color space including a hue component, a saturation component, and a brightness component, and obtaining a target frequency based on the frequency domain information, the hue component corresponding to the target frequency.
2. The imaging method of claim 1, wherein:
converting the first interference image set into two-dimensional data in the singular value decomposition of the first interference image set to obtain the time feature matrix and the singular value matrix; and performing the singular value decomposition on the two-dimensional data to represent the two-dimensional data as:
S＝U∑V，
s is the two-dimensional data, U is a space feature matrix, sigma is the singular value matrix, and V is the time feature matrix.
3. The imaging method of claim 1, wherein:
the threshold condition is that the cumulative zero-crossing rate of each time feature vector is larger than a preset value, wherein the preset value is 3 times the standard deviation of the cumulative zero-crossing rate.
4. The imaging method as claimed in claim 2, wherein:
reconstructing the first interference image set based on the updated singular value matrix and serving as the second interference image set, multiplying the spatial feature matrix, the updated singular value matrix and the time feature matrix to obtain updated two-dimensional data, and then obtaining the second interference image set corresponding to the first interference image set based on the updated two-dimensional data.
5. The imaging method of claim 1, wherein:
in the signal-to-noise ratio enhancement processing, an average value of pixel values in each sliding window is obtained, differences between the pixel values in each sliding window and the average value are integrated to obtain an integrated value, an absolute value of the integrated value is added to the preset length to obtain an average integrated value, and the average integrated value is used as the pixel value of the third interference image set.
6. The imaging method as set forth in claim 5, wherein:
the preset step length is a preset proportion of the preset length, and the preset proportion is smaller than 1.
7. A server comprising a processor and a memory for storing computer program instructions, the processor implementing the imaging method of any of claims 1 to 6 by executing the computer program instructions on the memory.
8. An imaging system based on artifact cancellation, comprising: the system comprises an acquisition module, an artifact elimination module, a signal-to-noise ratio enhancement module and a dynamic imaging module; the acquisition module is used for continuously acquiring a fresh sample by using a plane interferometer to acquire a plurality of interference images and form a first interference image set according to acquisition time, wherein the plane interferometer is used for measuring based on a double-beam equal-thickness interference principle; the artifact elimination module is configured to perform artifact elimination processing on the first interference image set to obtain a second interference image set, in the artifact elimination processing, perform singular value decomposition on the first interference image set to obtain a temporal feature matrix and a singular value matrix, each column of the temporal feature matrix is a temporal feature vector, calculate a fluctuation degree of the temporal feature vector, screen the temporal feature vector meeting a threshold condition based on the fluctuation degree to update singular values in the singular value matrix corresponding to the temporal feature vector to preset singular values, and reconstruct the first interference image set based on the updated singular value matrix and use the first interference image set as the second interference image set, and represent the fluctuation degree of the temporal feature vector by using a cumulative zero-crossing rate of the temporal feature vector, where a cumulative zero-crossing rate of an ith column of the temporal feature vector is represented as: D_ZRC i ＝|ZRC i+1 -ZRC i I, wherein i is the column index of the temporal feature matrix, ZRC i Zero crossing rate of the ith column time feature vector; the signal-to-noise ratio enhancement module performs signal-to-noise ratio enhancement processing on pixel values in each sliding window in the second interference image set by using a sliding window with a preset length to obtain a third interference image set, wherein the sliding window moves along the direction of the acquisition time according to a preset step length, and the first interference image set is obtained by using the sliding window with the preset lengthThe second interference image set is expressed as a three-dimensional matrix, the first dimension and the second dimension in the three dimensions correspond to the dimension of the interference image, the third dimension in the three dimensions corresponds to the acquisition time, and the dimension of the sliding window is consistent with the dimension of the second interference image set; and the dynamic imaging module acquires components of a three-dimensional color space based on the third interference image set, and converts the third interference image set into a color image based on the components of the three-dimensional color space, wherein the color image reflects an internal dynamic signal of the live sample and obtains the color image in a non-invasive way, in acquiring the components of the three-dimensional color space based on the third interference image set, a plurality of pixel values of the same position of a plurality of interferograms in the third interference image set are acquired to form a pixel sequence ordered according to the acquisition time, the components of the three-dimensional color space of each position are acquired based on time domain information and frequency domain information of the pixel sequence of each position, the components of the three-dimensional color space comprise a tone component, a saturation component and a brightness component, and a target frequency is acquired based on the frequency domain information, and the tone component corresponds to the target frequency.
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