Ultrasonic C scanning microscopic imaging method and system based on noise fluctuation optimization

Abstract

The invention relates to an ultrasonic C scanning microscopic imaging method and system based on noise fluctuation optimization. The method comprises the steps that object ultrasonic echo information is acquired, signal filtering processing is carried out on each object ultrasonic echo signal, corresponding object filtered echo signals are generated, object imaging layer gate features corresponding to the object filtered echo signals are extracted for each object filtered echo signal, and object imaging layer gate features corresponding to the object filtered echo signals are obtained. Performing feature processing on the object imaging layer gate features by using the constructed parameterized logistic regression function, at least generating an object imaging layer normalized value after feature processing, and generating a pixel gray value corresponding to the current object filtered echo signal based on the object imaging layer normalized value; and generating an ultrasonic C scanning image corresponding to the to-be-detected object based on the pixel gray values of the echo signals after all the objects are filtered. According to the method, the influence of noise fluctuation on ultrasonic imaging is optimized, and the signal-to-noise ratio and the resolution ratio of ultrasonic C scanning imaging are improved.

Description

Technical Field

[0001] The present invention relates to an ultrasonic C-scanning microscopic imaging method and system, in particular to an ultrasonic C-scanning microscopic imaging method and system based on noise fluctuation optimization. Background Art

[0002] Ultrasonic microscopy, as a nondestructive testing method, has been widely used in fields such as materials science and biomedicine. C-scan imaging captures ultrasonic echo signals at varying depths within a sample, enabling two-dimensional or three-dimensional imaging of the sample's internal structure. However, in practical applications, ultrasonic echo signals are susceptible to noise interference, particularly when using high-frequency probes and high sampling rates. This noise fluctuation can significantly degrade image quality and resolution.

[0003] Traditional C-scan imaging typically uses fixed filtering parameters and simple normalization, making it difficult to adapt to variations in probe frequency and sampling rate, and its effectiveness in suppressing noise fluctuations is limited. Furthermore, existing methods often ignore the impact of noise fluctuations on peak-to-peak values ​​when processing signals within the threshold, resulting in inaccurate grayscale calculations, which further affects imaging quality. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide an ultrasonic C-scan microscopy imaging method and system based on noise fluctuation optimization, which optimizes the influence of noise fluctuation on ultrasonic imaging and improves the signal-to-noise ratio and resolution of ultrasonic C-scan imaging.

[0005] According to the technical solution provided by the present invention, an ultrasonic C-scan microscopy imaging method based on noise fluctuation optimization, the ultrasonic C-scan microscopy imaging method includes: Providing an object to be inspected and an ultrasonic probe unit adapted for the object to be inspected, performing an ultrasonic C-scan on the object to be inspected using the ultrasonic probe unit, and generating ultrasonic echo information of the object after the ultrasonic C-scan, wherein the ultrasonic echo information of the object includes a plurality of ultrasonic echo signals of the object; performing signal filtering processing on each object ultrasonic echo signal to at least filter out high-frequency noise in each object ultrasonic echo signal and generate a corresponding object filtered echo signal, wherein filtering parameters used in the signal filtering processing are formed based on at least an object ultrasonic scanning state setting of the ultrasonic probe unit for ultrasonically scanning the object to be inspected; For each object filtered echo signal, extract the object imaging layer gate feature corresponding to the object filtered echo signal, and use the constructed parameterized logistic regression function to perform feature processing on the object imaging layer gate feature, so as to generate at least an object imaging layer normalization value after the feature processing, and generate a pixel grayscale value corresponding to the current object filtered echo signal based on the object imaging layer normalization value, wherein, The parameterized logistic regression function is generated based on at least a reference ultrasonic echo signal characteristic of a reference object, where the reference object is an object of the same type as the object to be inspected and has no defects. An ultrasonic C-scan image corresponding to the object to be inspected is generated based on the pixel grayscale values ​​of the filtered echo signals of all objects.

[0006] When performing signal filtering, a second-order Butterworth low-pass filter is used to filter the object ultrasonic echo signal, where: The filtering parameters of the second-order Butterworth low-pass filter include at least a cutoff frequency, wherein the cutoff frequency is formed based on an ultrasonic scanning state setting of the object to be inspected by the ultrasonic probe unit; The ultrasonic scanning state of the object at least includes the signal frequency of the ultrasonic detection signal and the sampling frequency of the ultrasonic echo signal of the object, wherein the ultrasonic detection signal is an ultrasonic signal vertically emitted by the ultrasonic probe unit toward the object to be detected.

[0007] For the cutoff frequency domain of the second-order Butterworth low-pass filter, we have:

[0008] in, is the cutoff frequency, is the signal frequency of the ultrasonic detection signal, The sampling frequency for generating the ultrasonic echo signal of the object.

[0009] For the constructed parameterized logistic regression function, we have:

[0010] in, is the parameterized logistic regression function, is the gate feature of the object imaging layer, is the logistic regression center base value, is the logistic regression central slope; When constructing a parameterized logistic regression function, the logistic regression center base value is set based on the reference ultrasonic echo signal characteristics of the reference object and / or logistic regression central slope .

[0011] Setting the logistic regression center base value based on the reference ultrasonic echo signal characteristics of the reference object and the logistic regression central slope When , there are:

[0012] in, is the maximum peak-to-peak value of the echo-free area in the reference ultrasonic echo signal, is the peak-to-peak value of the reference surface segment echo signal in the reference ultrasonic echo signal, is the peak value of the reference imaging layer echo in the reference ultrasonic echo signal; The reference ultrasonic echo signal is an ultrasonic echo signal generated by performing an ultrasonic A-scan on a reference object using an ultrasonic probe unit.

[0013] For the reference ultrasound echo signal, extract the peak-to-peak value of the reference imaging layer echo When , there are: Identifying a reference surface segment echo signal of a reference ultrasonic echo signal, and extracting a surface wave reference position corresponding to a positive peak value in the reference surface segment echo signal; Based on the surface wave reference position and the predetermined surface-imaging echo parameters, the reference imaging layer echo signal is extracted from the reference ultrasonic echo signal, wherein: The surface-imaging echo parameter is generated based on at least the relative position between the reference imaging layer in the reference object and the surface of the reference object; Based on the extracted reference imaging layer segment echo signal, the peak-to-peak value of the reference imaging layer echo is calculated.

[0014] For the maximum peak-to-peak value of the echo-free area in the reference ultrasonic echo signal, we have: For the reference ultrasonic echo signal, constructing a reference echo sliding window and a no-echo identification threshold, wherein the no-echo identification threshold is set based on at least a peak-to-peak value of the reference surface segment echo signal; Slide the reference echo sliding window on the reference ultrasonic echo signal along the sampling order of the reference ultrasonic echo signal, and calculate the peak-to-peak value of the sliding window area of ​​the corresponding sliding window ultrasonic echo signal in each reference echo sliding window. When the peak-to-peak value of the sliding window area is not greater than the no-echo recognition threshold, the current sliding window ultrasonic echo signal is identified as a no-echo area; The peak-to-peak value of each echo-free region is calculated, and the maximum value among all the calculated peak-to-peak values ​​of the echo-free region is taken as the maximum peak-to-peak value of the echo-free region.

[0015] For the gate features of the object imaging layer, we have:

[0016] in, is the peak value of the echo of the object imaging layer in the echo signal after object filtering, It is the peak value of the object surface echo in the echo signal after object filtering.

[0017] After the normalized value of the object imaging layer is generated, the normalized value of the object imaging layer is multiplied by 255, and the result of the multiplication is used as the pixel grayscale value corresponding to the echo signal after object filtering.

[0018] An ultrasonic C-scan microscopic imaging system based on noise fluctuation optimization includes an ultrasonic probe unit and an imaging processing unit, wherein: For any object to be inspected, an ultrasonic C-scan is performed on the object to be inspected using an ultrasonic probe unit to generate ultrasonic echo information of the object after the ultrasonic C-scan; The imaging processing unit processes the generated ultrasonic echo signal of the object using the imaging method described above and generates a corresponding ultrasonic C-scan image.

[0019] The advantages of the present invention are as follows: the ultrasonic echo signal of the object is subjected to signal filtering processing, so that the high-frequency noise in each ultrasonic echo signal of the object can be filtered out and the corresponding filtered echo signal of the object can be generated, thereby optimizing the influence of noise fluctuation on ultrasonic imaging; The object imaging layer gate features of the echo signal after object filtering are extracted, and then the object imaging layer gate features are feature processed using a parameterized logistic regression function to obtain the object imaging layer normalized value. The logistic regression normalization processing of the parameterized logistic regression function can significantly improve the contrast and resolution of the generated ultrasonic C-scan image, facilitating subsequent analysis and defect identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of an embodiment of ultrasonic C-scan microscopy imaging of the present invention.

[0021] Figure 2 This is a schematic diagram of an embodiment of extracting imaging layer echo band signals according to the present invention.

[0022] Figure 3 Schematic diagram of an embodiment of the echo-free region within the reference ultrasonic echo signal of the present invention.

[0023] Figure 4 Schematic diagram of an embodiment of an ultrasonic C-scan image according to the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described below with reference to specific drawings and embodiments.

[0025] In order to optimize the influence of noise fluctuation on ultrasonic imaging and improve the signal-to-noise ratio and resolution of ultrasonic C-scan imaging, the present invention provides an ultrasonic C-scan microscopic imaging method based on noise fluctuation optimization, the ultrasonic C-scan microscopic imaging method comprising: The application provides a to-be-inspected object and an ultrasonic probe unit matched with the to-be-inspected object, performs ultrasonic C scanning on the to-be-inspected object by using the ultrasonic probe unit, and generates object ultrasonic echo information after the ultrasonic C scanning, wherein the object ultrasonic echo information comprises a plurality of object ultrasonic echo signals. Each object ultrasonic echo signal is subjected to signal filtering processing to at least filter out high-frequency noise in each object ultrasonic echo signal, and a corresponding object filtered echo signal is generated, wherein the filtering parameter used in the signal filtering processing is at least set based on the object ultrasonic scanning state of the ultrasonic probe unit performing ultrasonic scanning on the to-be-inspected object. For each object filtered echo signal, the object imaging layer gate feature corresponding to the object filtered echo signal is extracted, and the object imaging layer gate feature is subjected to feature processing by using a constructed parameterized logistic regression function, so as to at least generate an object imaging layer normalized value, and a pixel gray value corresponding to the current object filtered echo signal is generated based on the object imaging layer normalized value, wherein The parameterized logistic regression function is at least constructed and generated based on the reference ultrasonic echo signal feature of a reference object, and the reference object is an object of the same type as the to-be-inspected object and has no defects. Based on the pixel gray values of all object filtered echo signals, an ultrasonic C scanning image corresponding to the to-be-inspected object is generated.

[0026] It can be understood that, when performing ultrasonic C scanning microscopic imaging, a to-be-inspected object and an ultrasonic probe unit should be provided, wherein the to-be-inspected object is an object to be detected by using ultrasonic scanning, and the type of the to-be-inspected object can be selected according to requirements, so as to meet the requirements of ultrasonic scanning detection. The ultrasonic probe unit can adopt a common form, and generally comprises an ultrasonic probe and an ultrasonic echo acquisition device for acquiring ultrasonic echo signals. The ultrasonic probe unit should be matched with the to-be-inspected object, that is, the ultrasonic probe unit can realize effective ultrasonic scanning detection on the to-be-inspected object. Therefore, after the to-be-inspected object is determined, a corresponding ultrasonic probe unit can be selected according to the requirements of ultrasonic imaging.

[0027] After the matched ultrasonic probe unit is selected, the to-be-inspected object can be subjected to ultrasonic C scanning in a common manner in the technical field, so as to obtain object ultrasonic echo information after the ultrasonic C scanning. Specifically, the object ultrasonic echo information comprises a plurality of object ultrasonic echo signals. The conditions of the object ultrasonic echo signals in the object ultrasonic echo information are related to the selected ultrasonic C scanning parameters, and the ultrasonic C scanning parameters can comprise scanning accuracy and scanning step length, etc. The conditions of the ultrasonic C scanning parameters can be selected according to requirements, so as to meet the requirements of ultrasonic C scanning on the to-be-inspected object.

[0028] It should be understood that the corresponding ultrasonic image can be generated by the object ultrasonic echo information, and the generated ultrasonic image is analyzed to realize the defect analysis, detection and other processing of the object to be detected. In order to improve the quality of ultrasonic imaging, thereby improving the accuracy and reliability of ultrasonic detection of the object to be detected, the generated object ultrasonic echo signal should be optimized for noise fluctuation, Figure 1 An embodiment of optimizing the noise fluctuation of the object ultrasonic echo signal is shown in the figure. As shown in the figure, when the noise fluctuation is optimized, at least signal filtering processing should be performed to at least filter out the high-frequency noise in each object ultrasonic echo signal.

[0029] Specifically, when performing signal filtering processing, signal filtering processing should be performed on each object ultrasonic echo signal in the object ultrasonic echo information, and the corresponding object filtered echo signal can be generated after signal filtering processing. It can be understood that the object filtered echo signal should not contain high-frequency noise, or only a small amount of high-frequency noise that has not been filtered. That is, after signal filtering processing, the influence of high-frequency noise on the ultrasonic echo signal can be effectively reduced, and therefore the accuracy of subsequent ultrasonic imaging can be improved.

[0030] In order to effectively suppress high-frequency noise, in an embodiment of the present application, the filtering parameters are at least set based on the object ultrasonic scanning state of the ultrasonic probe unit for ultrasonic scanning of the object to be detected. The following describes the manner of signal filtering processing.

[0031] In an embodiment of the present application, when performing signal filtering processing, a second-order Butterworth low-pass filter is used to filter the object ultrasonic echo signal, wherein, The filtering parameters of the second-order Butterworth low-pass filter at least include a cutoff frequency, wherein the cutoff frequency is set based on the object ultrasonic scanning state of the ultrasonic probe unit for ultrasonic scanning of the object to be detected. The object ultrasonic scanning state at least includes the signal frequency of the ultrasonic detection signal and the sampling frequency of the sampled object ultrasonic echo signal, wherein the ultrasonic detection signal is the ultrasonic signal vertically emitted by the ultrasonic probe unit to the object to be detected.

[0032] It should be understood that when a second-order Butterworth low-pass filter is used to filter each object's ultrasonic echo signal, high-frequency noise can be effectively suppressed based on the characteristics of the low-pass filter. To improve the reliability of high-frequency noise suppression and adapt to the signal frequency and sampling frequency of different ultrasonic probe units, the cutoff frequency of the second-order Butterworth low-pass filter of the present invention can be set based on the object's ultrasonic scanning state. Specifically, the object's ultrasonic scanning state can include at least the signal frequency and sampling frequency of the ultrasonic detection signal, wherein the signal frequency of the ultrasonic detection signal specifically refers to the pulse width of the ultrasonic detection signal, and the sampling frequency specifically refers to the frequency at which the ultrasonic echo acquisition device samples the echo and generates the object's ultrasonic echo signal.

[0033] It should be noted that when performing an ultrasonic C-scan on an object to be inspected, the ultrasonic probe's operating state can be set to cause it to transmit an ultrasonic detection signal perpendicularly toward the object to be inspected, and the corresponding signal frequency of the ultrasonic detection signal can be set. Therefore, the signal frequency of the ultrasonic detection signal can be obtained based on the set operating state of the ultrasonic probe. Furthermore, the operating state of the ultrasonic echo acquisition device can also be set. The operating state of the ultrasonic echo acquisition device can include at least a sampling frequency. When the sampling frequency is high, each object's ultrasonic echo signal includes more sampling points. The sampling frequency is related to the ultrasonic echo acquisition device. Therefore, the sampling frequency can be obtained based on the operating state of the ultrasonic echo acquisition device.

[0034] In one embodiment of the present invention, for the cutoff frequency domain of the second-order Butterworth low-pass filter, we have:

[0035] in, is the cutoff frequency, is the signal frequency of the ultrasonic detection signal, The sampling frequency for generating the ultrasonic echo signal of the object.

[0036] Specifically, after determining the cutoff frequency of the second-order Butterworth low-pass filter in the above manner, the quality factor Q of the second-order Butterworth low-pass filter should generally be set. The size of the quality factor Q of the second-order Butterworth low-pass filter can be selected as needed, such as the quality factor Q can be set to 0.707. It is understandable that after setting the cutoff frequency and quality factor Q of the second-order Butterworth low-pass filter, the second-order Butterworth low-pass filter can be used to perform low-pass filtering on the ultrasonic echo signal of the object. The specific low-pass method and process are consistent with the existing technology and will not be repeated here.

[0037] Depend on Figure 1It can be seen that after signal filtering is performed on each object ultrasonic echo signal, a filtered object echo signal can be generated. In order to generate an ultrasonic image, the filtered object echo signal should also be feature processed, and the feature processing at least includes logistic regression normalization processing.

[0038] In specific implementations, the present invention performs feature processing on each object's filtered echo signal by pre-constructing a parameterized logistic regression function. When the feature processing is logistic regression normalization, a normalized value for the object imaging layer can be generated during the feature processing. The object imaging layer is the target scanning detection layer within the object to be inspected. Therefore, when performing ultrasonic C-scan inspection on an object to be inspected, the primary focus is determining whether defects exist in the object imaging layer. The object imaging layer can generally be a cross-sectional layer within the object to be inspected.

[0039] When constructing a parameterized logistic regression function, the present invention utilizes the reference ultrasonic echo signal characteristics of a reference object. The reference object and the object to be inspected belong to the same class of objects, and the reference object is determined to be free of defects using methods known in the art. That is, the corresponding reference imaging layer within the reference object should be free of defects. It can be understood that the reference imaging layer within the reference object corresponds directly to the object imaging layer of the object to be inspected, i.e., they are cross-sectional layers of the same material class. Therefore, after determining the object imaging layer of the object to be inspected, the reference imaging layer of the reference object can be determined. Because the reference object and the object to be inspected belong to the same class of material, and the reference imaging layer of the reference object is free of defects, constructing a parameterized logistic regression function using the reference ultrasonic echo signal characteristics of the reference object and performing feature processing using the parameterized logistic regression function can achieve adaptive noise suppression and enhance anti-interference capabilities.

[0040] Specifically, the reference ultrasonic echo signal is an ultrasonic echo signal generated by performing an ultrasonic A-scan on the reference object using the ultrasonic probe unit. Therefore, the reference ultrasonic echo signal characteristics of the reference object specifically refer to the characteristics of the reference ultrasonic echo signal generated by performing an ultrasonic A-scan on the reference object using the above-mentioned ultrasonic probe unit. The scanning conditions for performing an ultrasonic A-scan on the reference object should be consistent with the scanning conditions for performing an ultrasonic C-scan on the object to be inspected, so that the reference ultrasonic echo signal and the object ultrasonic echo signal have good signal consistency, wherein the scanning conditions include at least the above-mentioned signal frequency and sampling frequency. In addition, the scanning conditions should also include necessary conditions such as the scanning height. The specific scanning conditions are no longer listed here one by one.

[0041] The characteristics of the reference ultrasonic echo signal and the object imaging layer gate characteristics of the echo signal after object filtering will be described in detail below.

[0042] In one embodiment of the present invention, for the constructed parameterized logistic regression function, we have:

[0043] wherein, is a parameterized logistic regression function, is an object imaging layer gate feature, is a logistic regression center base value, is a logistic regression center slope; when constructing the parameterized logistic regression function, setting the logistic regression center base value and / or the logistic regression center slope based on the reference ultrasonic echo signal feature of the reference object.

[0044] Specifically, as can be seen from the expression of the above-mentioned parameterized logistic regression function, when the object imaging layer gate feature is extracted, the value of the parameterized logistic regression function can be calculated, therefore, the value of the parameterized logistic regression function is the object imaging layer normalized value.

[0045] Further, when constructing the parameterized logistic regression function based on the reference ultrasonic echo signal feature of the reference object, specifically, setting the logistic regression center base value and / or the logistic regression center slope based on the reference ultrasonic echo signal feature of the reference object.

[0046] In an embodiment of the present application, when setting the logistic regression center base value and the logistic regression center slope based on the reference ultrasonic echo signal feature of the reference object, then:

[0047] wherein, is the maximum peak-to-peak value of the echo-free region in the reference ultrasonic echo signal, is the reference surface section echo signal peak-to-peak value in the reference ultrasonic echo signal, is the reference imaging layer echo peak-to-peak value in the reference ultrasonic echo signal.

[0048] As can be seen from the above description, when setting the logistic regression center base value and the logistic regression center slope based on the reference ultrasonic echo signal feature of the reference object, the maximum peak-to-peak value of the echo-free region in the reference ultrasonic echo signal , the reference surface section echo signal peak-to-peak value in the reference ultrasonic echo signal and the reference imaging layer echo peak-to-peak value in the reference ultrasonic echo signal , that is, the reference ultrasonic echo signal characteristics should at least include the maximum peak-to-peak value of the echo-free area in the reference ultrasonic echo signal , peak-to-peak value of echo signal in reference surface segment and the peak value of the echo of the reference imaging layer .

[0049] It is understandable that the logistic regression center base value Characterizes the corresponding state of the echo-free area in the reference ultrasonic echo signal, the reference imaging layer and the reference surface segment echo signal, so the imaging layer gate feature Close to the logistic regression center base value , it can be considered that the object imaging layer is close to the reference imaging layer, and when the imaging layer gate feature Deviation from the logistic regression center base value , it can be considered that the object imaging layer has a large deviation from the reference imaging layer. Therefore, it can be seen that the above-mentioned logistic regression center base value and the logistic regression central slope , which can improve the gate characteristics of the imaging layer The maximum peak-to-peak value in the echo-free area and the peak-to-peak value of the reference imaging layer echo The sensitivity between them can be enhanced, thereby enhancing the similarity between the object imaging layer and the reference imaging layer, reducing the sensitivity to noise, and improving the quality of subsequent ultrasonic C-scan images.

[0050] It should be noted that the reference ultrasonic echo signal can be generated by performing an ultrasonic A-scan on the reference object using an ultrasonic probe unit. The area / position of the ultrasonic A-scan should be able to represent the characteristics of the reference imaging layer of the reference material. For example, the area / position of the ultrasonic A-scan can be the central area or a smooth area of ​​the reference material. The specific location can be determined based on the corresponding characteristics of the object to be inspected and the reference object. The following describes in detail the method and process for extracting the characteristics of the reference ultrasonic echo signal of the reference object.

[0051] In one embodiment of the present invention, when extracting the peak value of the reference imaging layer echo from the reference ultrasonic echo signal, the following is obtained: Identifying a reference surface segment echo signal of a reference ultrasonic echo signal, and extracting a surface wave reference position corresponding to a positive peak value in the reference surface segment echo signal; Based on the surface wave reference position and the predetermined surface-imaging echo parameters, the reference imaging layer echo signal is extracted from the reference ultrasonic echo signal, wherein: The surface-imaging echo parameter is generated based on at least the relative position between the reference imaging layer in the reference object and the surface of the reference object; Based on the extracted echo signal of the reference imaging layer segment, the peak-to-peak value of the reference imaging layer echo is calculated.

[0052] As can be seen from the above description, the reference imaging layer is generally the interior of the reference object. The reference ultrasonic echo signal can represent the reflection state of the surface of the reference object, the reference imaging layer or other structural layers to the ultrasonic detection signal. Therefore, the reference ultrasonic echo signal should include the reference surface segment echo signal and the reference imaging layer segment echo signal. Figure 2 An embodiment of a reference ultrasonic echo signal is shown in FIG. Figure 2 In the figure, the horizontal axis is the sampling time and the vertical axis is the amplitude of the ultrasonic echo. It should be understood that Figure 2 The reference ultrasonic echo signal in is the intercepted reference ultrasonic echo signal, which can clearly express the corresponding states of the reference surface segment echo signal and the reference imaging layer segment echo signal.

[0053] It can be understood that the reference surface segment echo signal is generally the first pulse echo in the reference ultrasonic echo signal, and generally speaking, the reference surface segment echo signal has the largest amplitude. Therefore, the reference surface segment echo signal in the reference ultrasonic echo signal can be identified based on the signal amplitude. Figure 2 The echo signal in the first segment on the left side of the image is the reference surface segment echo signal. After determining the reference surface segment echo signal, the corresponding peak-to-peak value of the reference surface segment echo signal can be calculated. The peak-to-peak value of the reference surface segment echo signal is the difference between the positive and negative peak values ​​of the reference surface segment echo signal. Furthermore, when calculating the peak-to-peak value of the reference surface segment echo signal, the surface wave reference position corresponding to the positive peak within the reference surface segment echo signal can also be determined. The surface wave reference position corresponding to the positive peak is generally the corresponding sampling time within the reference ultrasonic echo signal.

[0054] After obtaining the surface wave reference position, the reference imaging layer segment echo signal can be extracted from the reference ultrasonic echo signal based on the predetermined surface-imaging echo parameters, wherein the reference imaging layer segment echo signal is the echo signal formed by the reference imaging layer reflecting the ultrasonic detection signal. Specifically, the surface-imaging echo parameters are generated based on at least the relative position state between the reference imaging layer in the reference object and the surface of the reference object. The relative position state may include the relative distance between the reference imaging layer and the surface of the reference object. According to the relative position state, the delay time of the reference imaging layer segment echo signal relative to the reference surface segment echo signal can generally be determined. Based on the determined delay time, the reference imaging layer segment echo signal can be extracted. Figure 2 The area marked with the data gate is the reference imaging segment echo signal.

[0055] It is understandable that after the reference imaging layer echo signal is extracted, the peak-to-peak value of the reference imaging layer echo can be calculated using the above method. The specific calculation method and process can be referred to the corresponding description above and will not be repeated here.

[0056] In one embodiment of the present invention, the maximum peak-to-peak value of the echo-free region in the reference ultrasonic echo signal is: For the reference ultrasonic echo signal, constructing a reference echo sliding window and a no-echo identification threshold, wherein the no-echo identification threshold is set based on at least a peak-to-peak value of the reference surface segment echo signal; Slide the reference echo sliding window on the reference ultrasonic echo signal along the sampling order of the reference ultrasonic echo signal, and calculate the peak-to-peak value of the sliding window area of ​​the corresponding sliding window ultrasonic echo signal in each reference echo sliding window. When the peak-to-peak value of the sliding window area is not greater than the no-echo recognition threshold, the current sliding window ultrasonic echo signal is identified as a no-echo area; The peak-to-peak value of each echo-free region is calculated, and the maximum value among all the calculated peak-to-peak values ​​of the echo-free region is taken as the maximum peak-to-peak value of the echo-free region.

[0057] It is understandable that when counting the number of no-echo peaks in the reference ultrasonic echo signal, the number of no-echo regions in the reference ultrasonic echo signal should be counted first. Figure 3 An embodiment of the no-echo region in the reference ultrasonic echo signal is shown in FIG. In order to statistically determine the no-echo region in the reference ultrasonic echo signal, a reference echo sliding window and a no-echo identification threshold should be set. The no-echo identification threshold can be set based on the peak-to-peak value of the reference surface segment echo signal. For example, the no-echo identification threshold can be 0.1* Of course, the no-echo recognition threshold can also be set to other situations, depending on whether the no-echo area can be effectively identified.

[0058] The reference echo sliding window is used to slide on the reference ultrasonic echo signal. The sliding direction is generally along the sampling order of generating the reference ultrasonic echo signal. It can be understood that the sampling order corresponds to the sampling time. For example, the sampling order should be Figure 2 and Figure 3 When the reference echo sliding window slides over the reference ultrasonic echo signal, one sampling interval may be spaced between two adjacent reference echo sliding windows. After determining the position of the reference echo sliding window, the sliding window ultrasonic echo signal corresponding to the reference echo sliding window can be obtained. Thereafter, the sliding window region peak-to-peak value of the sliding window ultrasonic echo signal can be calculated.

[0059] In specific implementation, the ultrasonic reflection characteristics of the reference object are used to configure the window size of the reference echo sliding window. According to the window size of the configured reference echo sliding window, for example, the window size (time length) of the reference echo sliding window can be the signal frequency of the ultrasonic detection signal. Corresponding to twice the period, when the calculated peak-to-peak value of the sliding window area is not greater than the no-echo recognition threshold, the current sliding window ultrasonic echo signal is identified as a no-echo area.

[0060] The sampling method described above slides the reference echo sliding window over the reference ultrasonic echo signal until it reaches its endpoint on the reference ultrasonic echo signal. Thereafter, the peak-to-peak value of each echo-free region is calculated, and the maximum of the calculated peak-to-peak values ​​of all echo-free regions is configured as the maximum peak-to-peak value of the echo-free region. Of course, other methods can also be used to calculate the number of maximum peak-to-peak values ​​of the echo-free regions, depending on whether the maximum peak-to-peak value requirement of the echo-free regions is met. These methods are not further detailed here.

[0061] In one embodiment of the present invention, the gate features of the object imaging layer are:

[0062] in, is the peak value of the echo of the object imaging layer in the echo signal after object filtering, It is the peak value of the object surface echo in the echo signal after object filtering.

[0063] Specifically, since the substance to be tested and the reference substance belong to the same type of substances, the corresponding methods for obtaining the peak-to-peak value of the reference imaging layer echo and the peak-to-peak value of the object surface echo can refer to the corresponding description above and will not be repeated here.

[0064] It should be noted that the peak value of the echo of the object imaging layer is used It can reflect the reflected energy of the echo at the imaging layer of the object in the echo signal after filtering. When there are defects or delaminations at the imaging layer of the object, the reflected energy is smaller than that without defects and delaminations. Since the amplitude of the surface echo is the largest, and the total energy of the echo signal after filtering of each object is fixed, the peak-to-peak value of the echo of the imaging layer of the object is calculated. Divide by the peak-to-peak value of the echo on the object surface , and when used as the gate feature of the object imaging layer, it can play the role of "the energy size of the relative surface echo", that is, to achieve the normalization of the object imaging layer echo energy relative to the surface wave energy.

[0065] In one embodiment of the present invention, after the object imaging layer normalization value is generated, the object imaging layer normalization value is multiplied by 255, and the result of the multiplication is used as the pixel grayscale value corresponding to the echo signal after object filtering.

[0066] Specifically, after obtaining the above-mentioned object imaging layer gate features for the object ultrasonic echo signal, the above-mentioned parameterized logistic regression function can be used to calculate the object imaging layer normalization value, and the object imaging layer normalization value is multiplied by 255. The product is the pixel grayscale value corresponding to the echo signal after current object filtering.

[0067] It is understandable that, by using the pixel grayscale values, the ultrasonic C-scan image of the object to be inspected after ultrasonic C-scanning can be obtained by using the commonly used technical means in this technical field. Figure 4 An embodiment of an ultrasonic C-scan image of an object to be inspected is shown in FIG.

[0068] From the above description, an ultrasonic C-scan microscopic imaging system based on noise fluctuation optimization can be obtained. In one embodiment of the present invention, the system includes an ultrasonic probe unit and an imaging processing unit, wherein: For any object to be inspected, an ultrasonic C-scan is performed on the object to be inspected using an ultrasonic probe unit to generate ultrasonic echo information of the object after the ultrasonic C-scan; The imaging processing unit processes the generated ultrasonic echo signal of the object using the imaging method described above and generates a corresponding ultrasonic C-scan image.

[0069] Specifically, the ultrasonic probe unit can be described above, and ultrasonic echo information of an object can be obtained using the ultrasonic probe unit. The imaging processing unit can utilize existing computer terminal equipment. The imaging processing unit should be capable of processing the ultrasonic echo signals of the object, i.e., performing the aforementioned signal filtering, feature processing, and generating ultrasonic C-scan images. For details, please refer to the above description and will not be repeated here.

Claims

1. An ultrasonic C-scan microscopy imaging method based on noise fluctuation optimization, characterized in that: The ultrasonic C-scan microscopy method comprises: Providing an object to be inspected and an ultrasonic probe unit adapted for the object to be inspected, performing an ultrasonic C-scan on the object to be inspected using the ultrasonic probe unit, and generating ultrasonic echo information of the object after the ultrasonic C-scan, wherein the ultrasonic echo information of the object includes a plurality of ultrasonic echo signals of the object; performing signal filtering processing on each object ultrasonic echo signal to at least filter out high-frequency noise in each object ultrasonic echo signal and generate a corresponding object filtered echo signal, wherein filtering parameters used in the signal filtering processing are formed based on at least an object ultrasonic scanning state setting of the ultrasonic probe unit for ultrasonically scanning the object to be inspected; For each object filtered echo signal, extract the object imaging layer gate feature corresponding to the object filtered echo signal, and use the constructed parameterized logistic regression function to perform feature processing on the object imaging layer gate feature, so as to generate at least an object imaging layer normalization value after the feature processing, and generate a pixel grayscale value corresponding to the current object filtered echo signal based on the object imaging layer normalization value, wherein, The parameterized logistic regression function is generated based on at least a reference ultrasonic echo signal characteristic of a reference object, where the reference object is an object of the same type as the object to be inspected and has no defects. An ultrasonic C-scan image corresponding to the object to be inspected is generated based on the pixel grayscale values ​​of the filtered echo signals of all objects.

2. The ultrasonic C-scan microscopy imaging method based on noise fluctuation optimization according to claim 1 is characterized by: performing During signal filtering, a second-order Butterworth low-pass filter is used to filter the ultrasonic echo signal of the object, where: The filtering parameters of the second-order Butterworth low-pass filter include at least a cutoff frequency, wherein the cutoff frequency is formed based on an ultrasonic scanning state setting of the object to be inspected by the ultrasonic probe unit; The ultrasonic scanning state of the object at least includes the signal frequency of the ultrasonic detection signal and the sampling frequency of the ultrasonic echo signal of the object, wherein the ultrasonic detection signal is an ultrasonic signal vertically emitted by the ultrasonic probe unit toward the object to be detected.

3. The ultrasonic C-scan microscopy imaging method based on noise fluctuation optimization according to claim 2, characterized in that: For the cutoff frequency domain of the second-order Butterworth low-pass filter, we have: in, is the cutoff frequency, is the signal frequency of the ultrasonic detection signal, The sampling frequency for generating the ultrasonic echo signal of the object.

4. The ultrasonic C-scan microscopy imaging method based on noise fluctuation optimization according to claim 1, characterized in that: For the constructed parameterized logistic regression function, we have: in, is the parameterized logistic regression function, is the gate feature of the object imaging layer, is the logistic regression center base value, is the logistic regression central slope; When constructing a parameterized logistic regression function, the logistic regression center base value is set based on the reference ultrasonic echo signal characteristics of the reference object and / or logistic regression central slope .

5. The ultrasonic C-scan microscopy imaging method based on noise fluctuation optimization according to claim 4, characterized in that: Setting the logistic regression center base value based on the reference ultrasonic echo signal characteristics of the reference object and the logistic regression central slope When , there are: in, is the maximum peak-to-peak value of the echo-free area in the reference ultrasonic echo signal, is the peak-to-peak value of the reference surface segment echo signal in the reference ultrasonic echo signal, is the peak value of the reference imaging layer echo in the reference ultrasound echo signal; The reference ultrasonic echo signal is an ultrasonic echo signal generated by performing an ultrasonic A-scan on a reference object using an ultrasonic probe unit.

6. The ultrasonic C-scan microscopy imaging method based on noise fluctuation optimization according to claim 5, characterized in that: For the reference ultrasonic echo signal, when extracting the peak value of the reference imaging layer echo, we have: Identifying a reference surface segment echo signal of a reference ultrasonic echo signal, and extracting a surface wave reference position corresponding to a positive peak value in the reference surface segment echo signal; Based on the surface wave reference position and the predetermined surface-imaging echo parameters, the reference imaging layer echo signal is extracted from the reference ultrasonic echo signal, wherein: The surface-imaging echo parameter is generated based on at least the relative position between the reference imaging layer in the reference object and the surface of the reference object; Based on the extracted echo signal of the reference imaging layer segment, the peak-to-peak value of the reference imaging layer echo is calculated.

7. The ultrasonic C-scan microscopy imaging method based on noise fluctuation optimization according to claim 5, characterized in that: For the maximum peak-to-peak value of the echo-free area in the reference ultrasonic echo signal, we have: For the reference ultrasonic echo signal, constructing a reference echo sliding window and a no-echo identification threshold, wherein the no-echo identification threshold is set based on at least a peak-to-peak value of the reference surface segment echo signal; Slide the reference echo sliding window on the reference ultrasonic echo signal along the sampling order of the reference ultrasonic echo signal, and calculate the peak-to-peak value of the sliding window area of ​​the corresponding sliding window ultrasonic echo signal in each reference echo sliding window. When the peak-to-peak value of the sliding window area is not greater than the no-echo recognition threshold, the current sliding window ultrasonic echo signal is identified as a no-echo area; The peak-to-peak value of each echo-free region is calculated, and the maximum value among all the calculated peak-to-peak values ​​of the echo-free region is taken as the maximum peak-to-peak value of the echo-free region.

8. The ultrasonic C-scan microscopy imaging method based on noise fluctuation optimization according to claim 5, characterized in that: For the gate features of the object imaging layer, we have: in, is the peak value of the echo of the object imaging layer in the echo signal after object filtering, It is the peak value of the object surface echo in the echo signal after object filtering.

9. The ultrasonic C-scan microscopy imaging method based on noise fluctuation optimization according to any one of claims 1 to 8, characterized in that: After the normalized value of the object imaging layer is generated, the normalized value of the object imaging layer is multiplied by 255, and the result of the multiplication is used as the pixel grayscale value corresponding to the echo signal after object filtering.

10. An ultrasonic C-scan microscopy imaging system based on noise fluctuation optimization, characterized in that: It includes an ultrasound probe unit and an imaging processing unit, wherein: For any object to be inspected, an ultrasonic C-scan is performed on the object to be inspected using an ultrasonic probe unit to generate ultrasonic echo information of the object after the ultrasonic C-scan; The imaging processing unit processes the generated ultrasonic echo signal of the object using the imaging method described in any one of claims 1 to 9 above, and generates a corresponding ultrasonic C-scan image.

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