Method and device for extracting maximum frequency of sound spectrum diagram and medical equipment

Abstract

The present application relates to the technical field of ultrasound, in particular to a method and device for extracting maximum frequency of a sound spectrum image and medical equipment, the method comprising: obtaining a target spectrum image; dividing the target spectrum image into target regions based on pixel values of each pixel point in the target spectrum image; filtering each target region based on pixel values of pixel points in each target region and a noise threshold to determine a target spectrum region; and extracting an envelope of the target spectrum region to determine a maximum frequency of the target spectrum image. By dividing the target spectrum image into target regions based on pixel values of pixel points, weak noise and weak signals can be distinguished and analyzed. Meanwhile, each target region is filtered to remove strong noise, so that the interference of noise on the analysis result is resisted, and the accuracy of the extracted maximum frequency is improved.

Description

Technical Field

[0001] This invention relates to the field of ultrasound technology, specifically to a method, apparatus, and medical device for extracting the maximum frequency of a spectrogram. Background Technology

[0002] In the field of medical ultrasound technology, the method of measuring blood flow velocity by acquiring an acoustic spectrogram using the Doppler effect has developed rapidly in recent years due to its non-invasive nature. When applying Doppler technology clinically, the obtained blood flow signal is first subjected to spectral analysis to obtain an acoustic spectrogram. Then, the spectrogram is used to calculate spectral parameters used for diagnosing vascular diseases, such as S / D, RI, and PI. Estimation of the maximum frequency is fundamental to the calculation of Doppler parameters; to obtain a small error, the maximum frequency curve must be accurately extracted.

[0003] To address this issue, some papers have proposed an improved percentage-based method called the hybrid method. This method first calculates the spectral integral curve of a single spectral line, then analyzes the integral curve. The hybrid method finds the intersection point of a predetermined straight line and the integral curve, and considers the frequency corresponding to this intersection point as the maximum frequency. However, it is sensitive to the signal-to-noise ratio (SNR) and it is difficult to determine an optimal percentage. Further, some papers have improved the hybrid method by proposing a geometric method. This method also first calculates the spectral integral curve of a single spectral line, then analyzes the integral curve, designs a straight line, and calculates the distance from each point on the integral curve to this line. The frequency corresponding to the point with the shortest distance is the maximum frequency. However, when the noise power is high, it may detect noise points on the maximum frequency curve. When the spectral signal is weak, it increases the estimation error, causing the maximum frequency curve to lose some blood flow signal.

[0004] However, noise has a significant impact on the maximum frequency extraction in both of the above methods, resulting in lower accuracy of the extracted maximum frequency curve and consequently causing the maximum frequency curve to lose some blood flow signals. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method, apparatus and medical device for extracting the maximum frequency of a spectrogram, in order to solve the problem of low accuracy in extracting the maximum frequency.

[0006] According to a first aspect, embodiments of the present invention provide a method for extracting the maximum frequency of a spectrogram, comprising:

[0007] Acquire the target spectral image;

[0008] Based on the pixel values ​​of each pixel in the target spectrum image, the target spectrum image is divided into regions to determine the target region;

[0009] Based on the pixel values ​​of pixels within each target region and the noise threshold, each target region is filtered to determine the target spectrum region;

[0010] Extract the envelope of the target spectral region to determine the maximum frequency of the target spectral image.

[0011] The method for extracting the maximum frequency from a spectrogram provided in this invention can distinguish weak noise and weak signals by dividing the pixel values ​​of the target spectrogram image into regions, and analyze both. At the same time, each target region is filtered to remove strong noise, so as to resist the interference of noise on the analysis results and improve the accuracy of the extracted maximum frequency.

[0012] In conjunction with the first aspect, in the first embodiment of the first aspect, the step of dividing the target spectrum image into regions based on the pixel values ​​of each pixel in the target spectrum image to determine the target region includes:

[0013] Obtain the number of target regions and the pixel threshold corresponding to each target region;

[0014] The pixel values ​​of each pixel in the target spectral image are compared with the pixel thresholds to divide each pixel and determine the target region.

[0015] The method for extracting the maximum frequency of the spectrogram provided in this embodiment of the invention determines the corresponding pixel threshold for each target region in order to accurately divide the pixels and ensure the reliability of the divided target regions.

[0016] In conjunction with the first embodiment of the first aspect, in the second embodiment of the first aspect, the step of comparing the pixel value of each pixel in the target spectral image with each pixel threshold, dividing each pixel, and determining the target region includes:

[0017] Pixels whose pixel values ​​are less than the minimum pixel threshold are identified as noise pixels and removed to determine the target region.

[0018] The method for extracting the maximum frequency of a spectrogram provided in this embodiment of the invention removes pixels with pixel values ​​less than the minimum pixel threshold, thereby achieving signal-to-noise separation.

[0019] In conjunction with the first aspect, in the third embodiment of the first aspect, the step of filtering each target region based on the relationship between the pixel values ​​of pixels within each target region and the corresponding threshold to determine the target spectrum region includes:

[0020] Based on the pixel values ​​of each pixel within the target region and the pixel values ​​of its neighboring pixels, a connected region is determined within all the target regions.

[0021] The scanning speed of the target spectral image is obtained to determine the area threshold of the noise region;

[0022] Based on the relationship between the area of ​​each connected region and the area threshold of the noise region, the connected regions are filtered to determine the target spectrum region.

[0023] The method for extracting the maximum frequency of a spectrogram provided in this invention identifies connected regions and then filters them. Connected regions with excessively small areas are considered noise, and removing these regions achieves the effect of removing strong independent speckle noise.

[0024] In conjunction with the third embodiment of the first aspect, in the fourth embodiment of the first aspect, the step of acquiring the scanning speed of the target spectral image to determine the area threshold of the noise region includes:

[0025] Obtain the initial threshold of the noise region at the preset scanning speed;

[0026] Based on the relationship between the scanning speed and the preset scanning speed, an adjustment coefficient is determined;

[0027] Based on the adjustment coefficient and the initial threshold, the area threshold of the noise region is determined.

[0028] The method for extracting the maximum frequency of a spectrogram provided in this invention addresses the issue that, at low scanning speeds, the spectrum is more dispersed, and noise signals are stretched, resulting in a larger area; conversely, at high scanning speeds, the spectrum is more concentrated, and noise signals are compressed, resulting in a smaller area. Therefore, by adjusting the area threshold of the noise region based on the correlation between scanning speed and a preset scanning speed, the accuracy of noise filtering can be ensured.

[0029] In conjunction with the first aspect, in the fifth embodiment of the first aspect, extracting the envelope of the target spectral region to determine the maximum frequency of the target spectral image includes:

[0030] Obtain the baseline in the target spectral image;

[0031] By utilizing the positional relationship between the baseline and the target spectrum region, the number of target spectrum regions above and below the baseline is determined;

[0032] Based on the number of target spectral regions above and below the baseline, the target spectral regions corresponding to the upper and lower envelopes are determined.

[0033] Based on the target spectral regions corresponding to the upper and lower envelopes, the upper and lower envelopes are extracted respectively to determine the maximum frequency of the target spectral image.

[0034] The method for extracting the maximum frequency of a spectrogram provided in this embodiment of the invention indicates that aliasing occurs when the number of target spectral regions above or below the baseline is greater than one. By determining the target spectral regions corresponding to the upper and lower envelopes while keeping the baseline unchanged, the maximum frequency can be adaptively extracted.

[0035] In conjunction with the fifth embodiment of the first aspect, in the sixth embodiment of the first aspect, the step of extracting the upper envelope and the lower envelope based on the target spectral regions corresponding to the upper envelope and the lower envelope, respectively, to determine the maximum frequency of the target spectral image, includes:

[0036] When there are two target spectral regions on the same side of the baseline, the target line is determined from the region between the two target spectral regions on the same side;

[0037] The envelope of the target image is extracted based on the target line and the corresponding target frequency region to determine the maximum frequency of the target spectrum image.

[0038] The method for extracting the maximum frequency of a spectrogram provided in this invention determines a target line in the region between two target spectral regions on the same side, thereby distinguishing the upper and lower envelopes on the same side and ensuring the automatic simultaneous extraction of the upper and lower envelope lines.

[0039] According to a second aspect, embodiments of the present invention also provide an apparatus for extracting the maximum frequency of a spectrogram, comprising:

[0040] The acquisition module is used to acquire the target spectrum image;

[0041] The segmentation module is used to segment the target spectrum image into regions based on the pixel values ​​of each pixel in the target spectrum image to determine the target region;

[0042] The filtering module is used to filter each of the target regions based on the pixel values ​​of the pixels in each target region and the noise threshold, and to determine the target spectrum region.

[0043] An extraction module is used to extract the envelope of the target spectral region to determine the maximum frequency of the target spectral image.

[0044] The spectrogram maximum frequency extraction device provided in this embodiment of the invention can distinguish weak noise and weak signals by dividing the pixel values ​​of the target spectrogram image into regions and analyzing both; at the same time, it filters each target region to remove strong noise, so as to resist the interference of noise on the analysis results and improve the accuracy of the extracted maximum frequency.

[0045] According to a third aspect, embodiments of the present invention provide a medical device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for extracting the maximum frequency of the spectrogram as described in the first aspect or any embodiment of the first aspect.

[0046] According to a fourth aspect, embodiments of the present invention provide a computer-readable storage medium storing computer instructions for causing the computer to perform the method for extracting the maximum frequency of a spectrogram as described in the first aspect or any embodiment of the first aspect. Attached Figure Description

[0047] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of the maximum frequency curve extracted by existing methods;

[0049] Figure 2 This is a flowchart of a method for extracting the maximum frequency of a spectrogram according to an embodiment of the present invention;

[0050] Figure 3 This is a flowchart of a method for extracting the maximum frequency of a spectrogram according to an embodiment of the present invention;

[0051] Figures 4a-4b This is a schematic diagram illustrating the determination of a connected region according to an embodiment of the present invention;

[0052] Figure 5 This is a flowchart of a method for extracting the maximum frequency of a spectrogram according to an embodiment of the present invention;

[0053] Figure 6 This is a schematic diagram of the maximum frequency curve of the spectrogram according to an embodiment of the present invention;

[0054] Figure 7This is a schematic diagram of the maximum frequency curve of the spectrogram according to an embodiment of the present invention;

[0055] Figure 8 This is a structural block diagram of a spectrogram maximum frequency extraction device according to an embodiment of the present invention;

[0056] Figure 9 This is a schematic diagram of the hardware structure of the medical device provided in an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Existing methods for extracting the maximum frequency from a spectrogram rely on integration. However, integration introduces noise accumulation, making these methods highly sensitive to noise; any noise present will affect the final extraction result. Therefore, the method for extracting the maximum frequency from a spectrogram provided in this invention divides the target frequency image into regions, filters out weak noise to obtain various target regions, and then further filters these target regions to determine the target spectral region, removing strong noise. Envelope extraction is then performed on this target spectral region, thus avoiding the influence of noise on the extracted maximum frequency.

[0059] Furthermore, existing extraction methods, whether percentage-based, over-threshold, or geometric, all require processing on a suitable spectrogram. If the spectrogram itself suffers from improper baseline adjustment leading to frequency aliasing, issues such as… Figure 1 The envelope identification shown is incorrect. Therefore, in the prior art, to obtain the correct envelope, the baseline position of the spectrogram must first be adjusted, then the upper envelope, lower envelope, or both envelopes must be identified, and then the corresponding method is used for envelope detection. However, this method requires baseline adjustment. To solve this problem, in this embodiment of the invention, the number of connected regions on the same side of the baseline is counted. If there are two connected regions on the same side of the baseline, it indicates that aliasing has occurred. If these two connected regions are above the baseline, the upper envelope is calculated for the spectrum closer to the baseline above the baseline, and the lower envelope is calculated for the spectrum farther from the baseline above the baseline. If these two connected regions are below the baseline, the lower envelope is calculated for the spectrum closer to the baseline below the baseline, and the upper envelope is calculated for the spectrum farther from the baseline below the baseline below the baseline. This achieves the effect of one-click envelope extraction and avoids baseline adjustment when aliasing occurs.

[0060] According to an embodiment of the present invention, a method for extracting the maximum frequency of a spectrogram is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0061] This embodiment provides a method for extracting the maximum frequency from a spectrogram, which can be used in medical devices, such as ultrasound equipment. Figure 2 This is a flowchart of a method for extracting the maximum frequency of a spectrogram according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps:

[0062] S11, acquire the target spectrum image.

[0063] The target spectral image can be obtained by medical devices through acquiring spectral data, or it can be obtained by medical devices from other devices. There are no restrictions on how the medical devices acquire the target spectral image; the method can be set according to actual needs. Taking the acquisition of spectral data by medical devices as an example, the medical device acquires a segment of Doppler signal that varies over time, filters out low-frequency information using a filter, and then performs Fourier transforms on the filtered data segments to obtain the spectral data, thus obtaining the target spectral image.

[0064] S12, Based on the pixel values ​​of each pixel in the target spectrum image, the target spectrum image is divided into regions to determine the target region.

[0065] Once the medical device acquires the target spectral image, it can determine the pixel value of each pixel. Based on the pixel values, cluster analysis can be performed to divide the target spectral image into regions; alternatively, the Otsu method can be used to divide each pixel of the target spectral image into regions, resulting in multiple target regions.

[0066] Specifically, medical devices can pre-define the pixel value range corresponding to each target region. The medical device then matches each pixel to its respective target region, thereby dividing the target spectral image into regions. The pixel value range corresponding to each target region can be determined based on empirical values, or obtained through optimization analysis, etc. By dividing the target spectral image into regions, the medical device can identify areas corresponding to weak noise and then filter them.

[0067] The specifics of this step will be described in detail below.

[0068] S13, based on the pixel values ​​of pixels in each target region and the noise threshold, filter each target region to determine the target spectrum region.

[0069] The target spectral image contains not only weak noise but also strong noise. Strong noise can persist for a period of time, appearing as a continuous spectral region on the target image. However, the area of ​​this continuous spectral region is relatively small compared to the area of ​​the signal region. Therefore, medical equipment can determine the connected regions within the target region based on the pixel values ​​of each pixel. By comparing the area of ​​the connected region with the corresponding noise threshold, strong noise can be filtered out, thereby determining the target spectral region.

[0070] Alternatively, medical devices can analyze the differences between each pixel and its neighboring pixels. If the difference is too large, the pixel can be considered an abnormal pixel and extracted to determine the target spectral region.

[0071] The specifics of this step will be described in detail below.

[0072] S14, extract the envelope of the target spectral region to determine the maximum frequency of the target spectral image.

[0073] After identifying the target spectral region, the medical device can extract the envelope within that region. As mentioned above, the number of target spectral regions on the same side of the baseline determines whether to extract the upper or lower envelope. The extracted envelope is the maximum frequency curve of the target spectral image.

[0074] The maximum frequency curve can then be used for blood flow signal analysis or other applications. No restrictions are placed on the applications based on the extracted maximum frequency curve; settings can be made according to actual needs.

[0075] The specifics of this step will be described in detail below.

[0076] The method for extracting the maximum frequency from the spectrogram provided in this embodiment can distinguish weak noise and weak signals by dividing the pixel values ​​of the target spectrogram image into regions and analyzing both. At the same time, each target region is filtered to remove strong noise, so as to resist the interference of noise on the analysis results and improve the accuracy of the extracted maximum frequency.

[0077] This embodiment provides a method for extracting the maximum frequency from a spectrogram, which can be used in medical devices, such as ultrasound equipment. Figure 3 This is a flowchart of a method for extracting the maximum frequency of a spectrogram according to an embodiment of the present invention, as shown below. Figure 3As shown, the process includes the following steps:

[0078] S21, acquire the target spectrum image.

[0079] Please see details Figure 2 The description of S11 in the illustrated embodiment will not be repeated here.

[0080] S22, Based on the pixel values ​​of each pixel in the target spectrum image, the target spectrum image is divided into regions to determine the target region.

[0081] Specifically, S22 includes:

[0082] S221, obtain the number of target regions and the pixel threshold corresponding to the target regions.

[0083] If there are m target regions in the target spectral image, then m-1 thresholds are needed to distinguish these target regions, with the thresholds being k1, ..., k2. n , ..., k m-1 The pixels within each target region can be represented as: C0 = {0, 1, ..., k1}, ..., C m ={k m+1 ,k m+2 ,...,k m+N The variance of these target regions is:

[0084] σ BC =ω0(μ0-μ r ) 2 +...+ω n (μ n -μ r ) 2 +...+ω m-1 (μ m-1 -μ r ) 2

[0085] in, P i It is the proportion occupied by pixel i, ω i It is the proportion of the i-th target region, μ i It is the pixel mean of the i-th target region, μ T It is the overall mean of all target regions. Medical devices will make σ BC The set of thresholds that yields the maximum value is the pixel threshold corresponding to each target region.

[0086] S222, compare the pixel value of each pixel in the target spectrum image with the pixel threshold, divide each pixel, and determine the target region.

[0087] After the medical device determines the pixel thresholds for each target region, it matches each pixel in the target spectral image with each pixel threshold to classify the pixels and categorize them into their corresponding target regions. Further, pixels with values ​​less than the minimum pixel threshold are identified as noise pixels and removed. Removing pixels with values ​​less than the minimum pixel threshold achieves signal-to-noise separation.

[0088] S23, based on the pixel values ​​of pixels in each target region and the noise threshold, filter each target region to determine the target spectrum region.

[0089] Specifically, S23 above includes:

[0090] S231, Based on the pixel values ​​of pixels in each target region and the pixel values ​​of adjacent pixels, determine the connected regions in all target regions.

[0091] For all target regions, the smallest unit is a pixel, and each pixel has 8 neighboring pixels. There are two common adjacency relationships: 4-adjacency and 8-adjacency. 4-adjacency involves 4 points: top, bottom, left, and right, such as... Figure 4a As shown; there are a total of 8 adjacent points, including points on the diagonal, such as... Figure 4b As shown, medical devices can determine the connected regions within a target area by analyzing the 4-adjacency or 8-adjacency of pixels within that area.

[0092] Of course, medical devices can also use other methods to analyze each target area and determine the connected areas within the target area.

[0093] S232, obtain the scanning speed of the target spectrum image to determine the area threshold of the noise region.

[0094] Scanning speed determines the area of ​​spectral data acquired per unit time; a faster scan speed results in a smaller area of ​​spectral data per unit time, while a slower scan speed results in a larger area of ​​spectral data per unit time. Therefore, it is necessary to obtain the scan speed of the target spectral image and determine the area threshold of the noise region based on this scan speed. A faster scan speed results in a smaller area threshold for the noise region, while a slower scan speed results in a larger area threshold for the noise region.

[0095] In some optional implementations of this embodiment, S232 may include:

[0096] (1) Obtain the initial threshold of the noise region at the preset scanning speed.

[0097] (2) Determine the adjustment coefficient based on the relationship between the scanning speed and the preset scanning speed.

[0098] (3) Determine the area threshold of the noise region based on the adjustment coefficient and the initial threshold.

[0099] Specifically, assuming the noise threshold at normal scanning speed is thre, when the scanning speed is low, the entire screen spectrum is relatively dispersed, and the noise signal will be stretched and the area is large. Therefore, the area threshold of the noise region is set to 1.4*thre. Conversely, when the scanning speed is high, the entire screen spectrum is relatively concentrated, and the noise signal will be compressed and the area is small. Therefore, the area threshold of the noise region is set to 0.6*thre.

[0100] Of course, the adjustment coefficients of 1.4 and 0.6 mentioned above are just examples, and can be adjusted according to actual needs. No restrictions are placed on them here.

[0101] When the scan speed is low, the spectrum is more dispersed, and the noise signal is stretched, resulting in a larger area. When the scan speed is high, the spectrum is more concentrated, and the noise signal is compressed, resulting in a smaller area. Therefore, by adjusting the area threshold of the noise region based on the trade-off between the scan speed and the preset scan speed, the accuracy of noise filtering can be guaranteed.

[0102] S233, based on the relationship between the area of ​​each connected region and the area threshold of the noise region, the connected regions are filtered to determine the target spectrum region.

[0103] Medical equipment compares the area of ​​each connected region with a noise region area threshold to determine whether each connected region is a strong noise region. If a connected region is a strong noise region, it is filtered out, thereby determining the target spectral region for subsequent envelope extraction.

[0104] S24, extract the envelope of the target spectral region to determine the maximum frequency of the target spectral image.

[0105] Please see details Figure 2 The description of S14 in the illustrated embodiment will not be repeated here.

[0106] The method for extracting the maximum frequency of a spectrogram provided in this embodiment determines the corresponding pixel threshold for each target region to accurately divide the pixels and ensure the reliability of the divided target regions. By identifying connected regions and then filtering them, connected regions with excessively small areas are considered noise and are removed, thereby achieving the effect of removing strong independent speckle noise.

[0107] This embodiment provides a method for extracting the maximum frequency from a spectrogram, which can be used in medical devices, such as ultrasound equipment. Figure 5This is a flowchart of a method for extracting the maximum frequency of a spectrogram according to an embodiment of the present invention, as shown below. Figure 5 As shown, the process includes the following steps:

[0108] S31, acquire the target spectrum image.

[0109] Please see details Figure 2 The description of S11 in the illustrated embodiment will not be repeated here.

[0110] S32, Based on the pixel values ​​of each pixel in the target spectrum image, the target spectrum image is divided into regions to determine the target region.

[0111] Please see details Figure 3 The description of S22 in the illustrated embodiment will not be repeated here.

[0112] S33, based on the pixel values ​​of pixels in each target region and the noise threshold, filters each target region to determine the target spectrum region.

[0113] Please see details Figure 3 The description of S23 in the illustrated embodiment will not be repeated here.

[0114] S34, extract the envelope of the target spectral region to determine the maximum frequency of the target spectral image.

[0115] Specifically, S34 includes:

[0116] S341, acquire the baseline in the target spectral image.

[0117] S342, using the positional relationship between the baseline and the target spectral region, determines the number of target spectral regions above and below the baseline.

[0118] Medical devices can record the location information of each target spectrum region. By comparing this location information with the location of the baseline, it can be determined whether the target spectrum region is above or below the baseline, and the number of target spectrum regions on the same side of the baseline.

[0119] S343, based on the number of target spectral regions above and below the baseline, determine the target spectral regions corresponding to the upper and lower envelopes.

[0120] The medical device first determines the number of connected regions retained above the baseline after denoising, i.e., the number of target spectral regions. If only one connected region is retained above the baseline (region A), and only one connected region is retained below the baseline (region B), then spectral aliasing has not occurred. Figure 6As shown, this is the general case. In this case, the upper envelope is calculated for the spectrum above the baseline, and the lower envelope is calculated for the spectrum below the baseline. If two connected regions remain above the baseline, namely region A and region B, and only one connected region remains below the baseline, namely region C, it indicates that the baseline is not in the optimal position, and there is aliasing in the spectrum. Figure 7 As shown, existing methods require adjusting the baseline to a suitable position before calculating the envelope. However, the method provided in this embodiment calculates the upper envelope for the spectrum above the baseline that is closer to it, and the lower envelope for the spectrum above the baseline that is farther from it. Unlike existing methods that require baseline adjustment before calculation, the method provided in this embodiment can automatically extract the upper and lower envelopes simultaneously with a single click. If two connected regions are retained above the baseline (regions A and B), and two connected regions are also retained below the baseline (regions C and D), it indicates aliasing of the upper and lower spectra. Existing methods cannot solve this problem. The method provided in this embodiment calculates the lower envelope for the spectrum above the baseline that is farther from it, and the upper envelope for the spectrum below the baseline that is farther from it, achieving the effect of extracting the envelope with a single click.

[0121] S344: Based on the target spectral regions corresponding to the upper and lower envelopes, extract the upper and lower envelopes respectively to determine the maximum frequency of the target spectral image.

[0122] After identifying the target spectral regions corresponding to the upper and lower envelopes, the medical device can extract the envelopes. Specifically, first, the top row L of the target spectral region is determined. Then, for a single spectral line in the target spectral region, the points that are continuously greater than a preset threshold from row L to the baseline are calculated. These are the maximum frequency points of that spectral line. Connecting the maximum frequency points of all spectral lines in the target spectral region forms the maximum frequency curve.

[0123] In some optional implementations of this embodiment, S344 may include:

[0124] (1) When there are two target spectral regions on the same side of the baseline, the target line is determined from the region between the two target spectral regions on the same side.

[0125] (2) Extract the envelope based on the target line and the corresponding target frequency region to determine the maximum frequency of the target spectrum image.

[0126] like Figure 7As shown, there are two target spectral regions above the baseline. The target spectral region above the baseline, closer to the baseline, is called region A, and the target spectral region above the baseline, farther from the baseline, is called region B. The medical device determines the target line from the blank area between region A and region B, namely the topmost row L mentioned above. The upper envelope is extracted using the target spectral region between the topmost row L and the baseline; the lower envelope is extracted using the target spectral region between the topmost row L and the boundary line of region B.

[0127] The target line is determined in the region between two target spectral regions on the same side to distinguish the upper and lower envelopes on the same side, ensuring that the upper and lower envelope lines are automatically extracted simultaneously.

[0128] The method for extracting the maximum frequency from a spectrogram provided in this embodiment addresses the issue of aliasing when the number of target spectral regions above or below the baseline exceeds one. By determining the target spectral regions corresponding to the upper and lower envelopes while maintaining the baseline constant, adaptive extraction of the maximum frequency can be achieved. Specifically, this embodiment employs region segmentation and other methods to preprocess the target spectral image, extracting the two regions with the largest areas. The upper envelope is calculated using the region above and close to the baseline, while the lower envelope is calculated using the region above or below the baseline. This method accurately obtains the maximum frequency curve for spectral data under both strong and weak noise conditions, unaffected by the baseline position. It effectively resists noise interference in strong noise conditions and effectively detects weak blood flow signals in weak noise conditions, avoiding signal loss. It possesses advantages such as adaptability, real-time processing capability, and strong practicality.

[0129] This embodiment also provides a device for extracting the maximum frequency of a spectrogram, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0130] This embodiment provides a device for extracting the maximum frequency of a spectrogram, such as... Figure 8 As shown, it includes:

[0131] Acquisition module 41 is used to acquire the target spectrum image;

[0132] The segmentation module 42 is used to segment the target spectrum image into regions based on the pixel values ​​of each pixel in the target spectrum image to determine the target region;

[0133] Filtering module 43 is used to filter each target region based on the pixel value of each pixel point in each target region and the noise threshold to determine the target spectrum region;

[0134] Extraction module 44 is used to extract the envelope of the target spectral region to determine the maximum frequency of the target spectral image.

[0135] The spectrogram maximum frequency extraction device provided in this embodiment can distinguish weak noise and weak signals by dividing the pixel values ​​of the target spectrogram image into regions and analyzing both. At the same time, it filters each target region to remove strong noise, so as to resist the interference of noise on the analysis results and improve the accuracy of the extracted maximum frequency.

[0136] In this embodiment, the device for extracting the maximum frequency of the spectrogram is presented in the form of a functional unit. Here, a unit refers to an ASIC circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0137] Further functional descriptions of the above modules are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0138] This invention also provides a medical device having the above-described features. Figure 8 The device shown is for extracting the maximum frequency of the spectrogram.

[0139] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a medical device provided in an optional embodiment of the present invention, such as... Figure 9 As shown, the medical device may include: at least one processor 51, such as a CPU (Central Processing Unit), at least one communication interface 53, a memory 54, and at least one communication bus 52. The communication bus 52 is used to enable communication between these components. The communication interface 53 may include a display screen or a keyboard; optionally, the communication interface 53 may also include a standard wired interface or a wireless interface. The memory 54 may be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 54 may also be at least one storage device located remotely from the aforementioned processor 51. The processor 51 may be combined with... Figure 8 The described apparatus has an application program stored in memory 54, and the processor 51 calls the program code stored in memory 54 to perform any of the above method steps.

[0140] The communication bus 52 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 52 can be divided into a blocking bus, a data bus, a control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0141] The memory 54 may include volatile memory, such as random-access memory (RAM); the memory may also include non-volatile memory, such as flash memory, hard disk drive (HDD) or solid-state drive (SSD); the memory 54 may also include a combination of the above types of memory.

[0142] The processor 51 can be a central processing unit (CPU), a network processor (NP), or a combination of CPU and NP.

[0143] The processor 51 may further include a hardware chip. This hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0144] Optionally, memory 54 is also used to store program instructions. Processor 51 can invoke program instructions to implement the method for extracting the maximum frequency of the spectrogram as shown in any embodiment of this application.

[0145] This invention also provides a non-transitory computer storage medium storing computer-executable instructions that can execute the method for extracting the maximum frequency of the spectrogram in any of the above method embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0146] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for extracting the maximum frequency of a spectrogram, characterized in that, include: Acquire the target spectral image; Based on the pixel values ​​of each pixel in the target spectrum image, the target spectrum image is divided into regions to determine the target region; Based on the pixel values ​​of each pixel within the target region and the pixel values ​​of its neighboring pixels, a connected region is determined within all the target regions. The scanning speed of the target spectral image is obtained to determine the area threshold of the noise region; Based on the relationship between the area of ​​each connected region and the area threshold of the noise region, the connected regions are filtered to determine the target spectrum region; Extract the envelope of the target spectral region to determine the maximum frequency of the target spectral image.

2. The method according to claim 1, characterized in that, The step of dividing the target spectrum image into regions based on the pixel values ​​of each pixel in the target spectrum image to determine the target region includes: Obtain the number of target regions and the pixel threshold corresponding to each target region; The pixel values ​​of each pixel in the target spectral image are compared with the pixel thresholds to divide each pixel and determine the target region.

3. The method according to claim 2, characterized in that, The step of comparing the pixel values ​​of each pixel in the target spectral image with each pixel threshold, dividing each pixel, and determining the target region includes: Pixels whose pixel values ​​are less than the minimum pixel threshold are identified as noise pixels and removed to determine the target region.

4. The method according to claim 1, characterized in that, The step of acquiring the scanning speed of the target spectral image to determine the area threshold of the noise region includes: Obtain the initial threshold of the noise region at the preset scanning speed; Based on the relationship between the scanning speed and the preset scanning speed, an adjustment coefficient is determined; Based on the adjustment coefficient and the initial threshold, the area threshold of the noise region is determined.

5. The method according to claim 1, characterized in that, Extracting the envelope of the target spectral region to determine the maximum frequency of the target spectral image includes: Obtain the baseline in the target spectral image; By utilizing the positional relationship between the baseline and the target spectrum region, the number of target spectrum regions above and below the baseline is determined; Based on the number of target spectral regions above and below the baseline, the target spectral regions corresponding to the upper and lower envelopes are determined. Based on the target spectral regions corresponding to the upper and lower envelopes, the upper and lower envelopes are extracted respectively to determine the maximum frequency of the target spectral image.

6. The method according to claim 5, characterized in that, The step of extracting the upper and lower envelopes based on the target spectral regions corresponding to the upper and lower envelopes to determine the maximum frequency of the target spectral image includes: When there are two target spectral regions on the same side of the baseline, the target line is determined from the region between the two target spectral regions on the same side; The envelope of the target spectrum image is extracted based on the target line and the corresponding target spectrum region to determine the maximum frequency of the target spectrum image.

7. A device for extracting the maximum frequency of a spectrogram, characterized in that, include: The acquisition module is used to acquire the target spectrum image; The segmentation module is used to segment the target spectrum image into regions based on the pixel values ​​of each pixel in the target spectrum image to determine the target region; The filtering module is used to determine connected regions within all the target regions based on the pixel values ​​of pixels in each target region and the pixel values ​​of adjacent pixels; The scanning speed of the target spectrum image is acquired to determine the area threshold of the noise region; based on the relationship between the area of ​​each connected region and the area threshold of the noise region, the connected regions are filtered to determine the target spectrum region. An extraction module is used to extract the envelope of the target spectral region to determine the maximum frequency of the target spectral image.

8. A medical device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for extracting the maximum frequency of the spectrogram as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method for extracting the maximum frequency of the spectrogram as described in any one of claims 1-6.

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