A breast lesion matching method and system based on ultrasound images

Abstract

The present invention discloses a breast lesion matching method and system based on ultrasound images, and relates to the field of breast lesion matching technology, and specifically comprises the following steps: obtaining echo interference analysis information from extracted lesion detection information, and analyzing it after obtaining it, and judging whether the boundary and morphology of the breast lesion will lead to inaccurate recognition due to echo interference; according to the judgment result, constructing an echo suppression mechanism for suppressing the influence of echo interference on lesion recognition, and applying it to the image processing flow after construction; in the process of applying the echo suppression mechanism, obtaining echo suppression feedback information in real time, and analyzing it after obtaining it, and evaluating whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and adjusting the echo suppression mechanism according to the evaluation result. The present invention solves the problem that breast lesions are difficult to distinguish from vascular echo interference, and improves the accuracy of lesion recognition.

Description

Technical Field

[0001] The present invention relates to the technical field of breast lesion matching, and in particular to a breast lesion matching method and system based on ultrasound images. Background Art

[0002] Breast lesions refer to abnormal areas in breast tissue, which may appear as lumps, cysts or calcifications. These lesions may be benign lesions or early signs of breast cancer. The purpose of matching breast lesions is to evaluate the changes in lesions by comparing current and previous medical images, including differences in location, size, shape, etc., so that doctors can detect the deterioration of the disease or the formation of new lesions early, and make more accurate diagnosis and treatment decisions. The breast lesion matching method based on ultrasound images has the advantages of being non-invasive, simple to operate, and real-time dynamic monitoring. It is particularly suitable for soft tissue imaging of the breast and can more accurately detect changes in the characteristics of lesions, which helps to improve the accuracy of early screening and diagnosis and reduce unnecessary invasive examinations.

[0003] Existing breast lesion matching technology based on ultrasound images achieves lesion matching through a series of image processing and feature extraction steps. First, multiple slice images of breast tissue are obtained through ultrasound equipment. These images are preprocessed, such as noise filtering and contrast enhancement, to ensure that the image quality is suitable for analysis. Next, the image segmentation algorithm or deep learning model is used to detect the suspicious lesion area in the image and extract its feature information, such as the shape, size, boundary and position of the lesion. These features are digitized and stored for comparison with the lesion information in the previous ultrasound image. The matching process determines the similarity between the current lesion and the previous lesion by calculating the similarity of the lesion shape, the deviation of the position and the consistency of the edge contour. In order to improve the matching accuracy, the existing technology usually adopts multi-dimensional feature matching, such as comprehensively analyzing the change trend of the lesion through geometric features, texture features, grayscale distribution, etc., and finally generates a matching evaluation to help doctors determine whether the lesion is stable, deteriorating or new. With this technology, doctors can accurately track the dynamic changes of lesions and adjust the diagnosis and treatment plan in time.

[0004] The prior art has the following deficiencies:

[0005] When the location of the breast lesion is close to the breast blood vessels, the ultrasound will produce strong interference with the echo signal of the blood vessels when passing through the breast tissue, especially when the distance between the lesion and the blood vessel is close, the echo interference will form a highlight area similar to the lesion. Because this echo overlaps with the actual shape and position of the lesion, the edge of the lesion in the image becomes blurred, and even part of the shape of the lesion is covered or distorted. The existing breast lesion matching technology based on ultrasound images mainly relies on the brightness information of the image for lesion identification and matching, but this technology lacks a special processing mechanism when the lesion overlaps with the blood vessel echo, and cannot effectively distinguish between the real lesion and the interference signal, resulting in the boundary of the lesion cannot be accurately captured. In this case, the existing technology cannot accurately extract the true shape and position of the lesion, and it is easy to mistake the blood vessel echo for part of the lesion, resulting in significant errors in the matching results. Ultimately, this error will affect the tracking and monitoring of lesion changes, causing doctors to make wrong judgments on the actual situation of the lesion, unable to accurately evaluate the progress of the lesion or the treatment effect, and affect the reliability of data analysis and subsequent processing.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a method and system for breast lesion matching based on ultrasound images to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a breast lesion matching method based on ultrasound image, specifically comprising the following steps:

[0009] Scan breast tissue with ultrasound equipment to obtain image information of breast lesions in real time, and extract lesion detection information from the image information for subsequent image processing procedures;

[0010] Acquiring echo interference analysis information from the extracted lesion detection information, and analyzing it after acquisition to determine whether the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference;

[0011] According to the judgment results, an echo suppression mechanism is constructed to suppress the influence of echo interference on lesion identification, and after construction, it is applied to the image processing process;

[0012] During the application of the echo suppression mechanism, the echo suppression feedback information is obtained in real time and analyzed after acquisition to evaluate whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and the echo suppression mechanism is adjusted according to the evaluation results;

[0013] Lesion matching is completed according to the adjusted echo suppression mechanism, and the results of lesion matching and the application process of the echo suppression mechanism are recorded and stored for subsequent monitoring of lesion changes and further analysis and optimization.

[0014] Preferably, the echo interference analysis information is obtained from the extracted lesion detection information, and analyzed after the acquisition to determine whether the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference, which specifically includes the following steps:

[0015] Acquiring echo interference analysis information from the extracted lesion detection information and performing preprocessing after acquisition;

[0016] Extracting echo signal fluctuation information and echo propagation anomaly information from the preprocessed echo interference analysis information, and analyzing them after extraction to generate interference signal intensity fluctuation coefficient and echo signal propagation anomaly index respectively;

[0017] The generated interference signal intensity fluctuation coefficient and echo signal propagation abnormality index are used to construct an interference recognition model to generate an interference coefficient. The generated interference coefficient is compared with a pre-set interference coefficient threshold. Based on the comparison results, it is determined whether the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference.

[0018] Preferably, the acquisition logic of the interference signal strength fluctuation coefficient and the echo signal propagation anomaly index is as follows:

[0019] Extract the echo signal fluctuation information from the preprocessed echo interference analysis information, specifically including the echo signal interference strength at different times within a period of time and the time interval between adjacent times, and calibrate the echo signal interference strength at different times within a period of time and the time interval between adjacent times as and , Indicates a period of time The echo signal interference strength at the time, , is a positive integer;

[0020] Calculate the interference signal strength fluctuation coefficient. The specific calculation formula is as follows:

[0021]

[0022] In the formula, is the interference signal strength fluctuation coefficient;

[0023] The echo propagation anomaly information in the preprocessed echo interference analysis information is extracted, including the actual propagation time of the echo signal, the theoretical propagation time, and the amplitude values ​​of the echo signal at different times during the propagation process, and calibrated as , and , represents the actual propagation time of the echo signal, represents the theoretical propagation time of the echo signal, Indicates the time period during which the echo signal propagates The amplitude value at the moment, , is a positive integer;

[0024] Calculate the echo signal propagation anomaly index. The specific calculation formula is as follows:

[0025]

[0026] In the formula, is the echo signal propagation anomaly index, It is the amplitude value of the echo signal at the initial moment during the propagation process.

[0027] Preferably, the generated interference signal strength fluctuation coefficient and echo signal propagation anomaly index Construct an interference recognition model and generate interference coefficients through weighted summation , and the generated interference coefficient The interference coefficient threshold is set Compare and judge whether the boundary and shape of breast lesions will be inaccurately identified due to echo interference based on the comparison results. The specific comparison analysis is as follows:

[0028] like , the boundaries and morphology of breast lesions will not be inaccurately identified due to echo interference;

[0029] like , the boundaries and morphology of breast lesions can lead to inaccurate identification due to echo interference.

[0030] Preferably, based on the judgment result, an echo suppression mechanism for suppressing the influence of echo interference on lesion identification is constructed, and after construction, it is applied to the image processing process, specifically including: when the judgment result is that the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference, by analyzing the intensity and propagation characteristics of the echo signal, the spatiotemporal distribution characteristics of the interference signal are extracted, and an echo suppression mechanism is constructed based on these characteristics; the mechanism includes frequency domain separation of the interference signal, signal intensity normalization processing, and a signal suppression strategy based on time and space distribution characteristics; by distinguishing and processing the interference signal and the effective signal, the influence of the interference signal on the image generation process is reduced; the constructed echo suppression mechanism is applied to the image processing process, and an ultrasound image with suppressed interference is generated through the signal processed data, and the mechanism is continuously applied in the image processing process to optimize the recognition result.

[0031] Preferably, during the application of the echo suppression mechanism, the echo suppression feedback information is obtained in real time, and analyzed after acquisition to evaluate whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and the echo suppression mechanism is adjusted according to the evaluation results, which specifically includes the following steps:

[0032] During the application of the echo suppression mechanism, the echo suppression feedback information is obtained in real time and preprocessed after being obtained;

[0033] Extracting comprehensive information of echo signal characteristics from the preprocessed echo suppression feedback information, and analyzing it after extraction to generate echo signal strength attenuation coefficient and signal strength fluctuation mean square coefficient respectively;

[0034] The generated echo signal intensity attenuation coefficient and signal intensity fluctuation mean square coefficient are used to construct a suppression effect model to generate a suppression coefficient. The generated suppression coefficient is compared with the pre-set suppression coefficient threshold. Based on the comparison results, it is evaluated whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and the echo suppression mechanism is adjusted based on the evaluation results.

[0035] Preferably, the acquisition logic of the echo signal strength attenuation coefficient and the signal strength fluctuation mean square coefficient is as follows:

[0036] Extract the comprehensive information of echo signal characteristics in the preprocessed echo suppression feedback information, including the strength, frequency, phase and corresponding time points of the interference signal at different times during the application of the echo suppression mechanism, and use the function to calculate the strength, frequency and phase of the interference signal at different times during the application of the echo suppression mechanism according to the time series. , and To express, For time point, Indicates the period of time during which the echo suppression mechanism is applied. The strength of the interference signal at all times, Indicates the period of time during which the echo suppression mechanism is applied. The frequency of the interference signal at all times, Indicates the period of time during which the echo suppression mechanism is applied. The phase of the interference signal at each moment, and the time period is defined as ;

[0037] Calculate the echo signal strength attenuation coefficient. The specific calculation formula is as follows:

[0038]

[0039] In the formula, is the echo signal strength attenuation coefficient, is the strength of the interference signal at the initial moment during the application of the echo suppression mechanism. is the frequency of the interfering signal at the initial moment during a period of time during the application of the echo suppression mechanism;

[0040] Calculate the mean square coefficient of signal strength fluctuation. The specific calculation formula is as follows:

[0041]

[0042] In the formula, is the mean square coefficient of signal intensity fluctuation, It indicates the rate of change of the intensity of the interference signal at different times over a period of time during the application of the echo suppression mechanism. It indicates the rate of change of the phase of the interference signal at different times over a period of time during the application of the echo suppression mechanism.

[0043] Preferably, the generated echo signal strength attenuation coefficient and signal strength fluctuation mean square coefficient Construct an inhibition effect model and generate an inhibition coefficient by weighted summation , and the resulting suppression coefficient With the preset suppression coefficient threshold A comparison is performed to evaluate whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas based on the comparison results, and the echo suppression mechanism is adjusted based on the evaluation results. The specific comparison analysis is as follows:

[0044] like , the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and there is no need to adjust the echo suppression mechanism;

[0045] like The suppression effect of the echo suppression mechanism cannot meet the requirements of distinguishing breast lesions from echo interference areas, and the echo suppression mechanism needs to be adjusted, including: by analyzing the intensity, frequency and phase changes of the current echo signal, readjusting the filtering parameters of the echo signal, optimizing the frequency domain separation algorithm to further eliminate frequency interference, and at the same time, for the interval with large signal intensity fluctuations, enhancing the intensity normalization processing, reducing the signal fluctuation amplitude, and recalibrating the interference suppression strategy in combination with the distribution characteristics of time and space.

[0046] Preferably, a breast lesion matching system based on ultrasound images comprises an ultrasound image real-time acquisition module, an echo interference analysis module, an echo suppression construction module, an echo suppression feedback evaluation module, and a lesion matching and data storage module;

[0047] Ultrasonic image real-time acquisition module, which scans breast tissue through ultrasonic equipment, obtains image information of breast lesions in real time, and extracts lesion detection information for subsequent image processing flow from the image information;

[0048] The echo interference analysis module obtains the echo interference analysis information from the extracted lesion detection information, and performs analysis after obtaining it to determine whether the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference;

[0049] An echo suppression construction module constructs an echo suppression mechanism for suppressing the influence of echo interference on lesion identification according to the judgment result, and applies it to the image processing flow after construction;

[0050] The echo suppression feedback evaluation module obtains echo suppression feedback information in real time during the application of the echo suppression mechanism, analyzes it after acquisition, evaluates whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and adjusts the echo suppression mechanism according to the evaluation results;

[0051] The lesion matching and data storage module completes lesion matching according to the adjusted echo suppression mechanism, and records and stores the results of lesion matching and the application process of the echo suppression mechanism for subsequent monitoring of lesion changes and further analysis and optimization.

[0052] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0053] 1. The present invention significantly improves the recognition accuracy of lesion boundaries and morphology in ultrasound images. The prior art relies on the brightness information of the image. When the lesion overlaps with the vascular echo signal, it is difficult to distinguish the lesion from the interference signal, resulting in blurred boundaries and even distorted morphology. By acquiring lesion detection information in real time and performing echo interference analysis, the present invention effectively eliminates the highlighted interference area in the image by introducing an interference suppression mechanism, thereby ensuring accurate distinction between lesions and interference signals. This process not only improves the edge clarity of the lesion, but also reduces misjudgment, ensuring that doctors can make diagnosis and treatment decisions based on more accurate images.

[0054] 2. The present invention obtains echo suppression feedback information in real time, evaluates whether the suppression effect meets the preset requirements, and adjusts the suppression mechanism according to the evaluation results. This dynamic optimization based on real-time data feedback ensures that echo interference is always effectively controlled throughout the entire image processing process, and ensures that even under different scanning conditions or individual differences among patients, echo interference will not affect the final lesion identification results. This dynamic adjustment mechanism ensures the adaptability of the technical solution and can cope with complex echo interference problems in different situations.

[0055] 3. The advantage of the present invention is the storage and subsequent analysis support of the lesion matching results. It is mentioned in the present invention that all lesion matching results and the application process of the echo suppression mechanism will be recorded and stored, providing reliable data support for the monitoring and analysis of subsequent lesion changes. The long-term recording and storage of such data can not only help doctors track lesion changes more accurately in future treatment processes, but also provide important references for future technical optimization. This design combining data storage and analysis greatly enhances the continuous optimization capability of the system, and can provide technical support for the long-term monitoring and treatment of breast lesions. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0057] Figure 1 The present invention is a flow chart of a method and system for matching breast lesions based on ultrasound images.

[0058] Figure 2 The present invention is a schematic diagram of a module of a breast lesion matching method and system based on ultrasound images. DETAILED DESCRIPTION

[0059] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0060] The present invention provides Figure 1 The method for matching breast lesions based on ultrasound images shown in the figure specifically comprises the following steps:

[0061] Scan breast tissue with ultrasound equipment to obtain image information of breast lesions in real time, and extract lesion detection information from the image information for subsequent image processing procedures;

[0062] "Scanning breast tissue with ultrasound equipment and obtaining real-time image information of breast lesions" can be achieved by configuring a high-frequency ultrasound probe and combining it with an automated image acquisition system. The ultrasound probe transmits sound waves through the breast tissue, and the echo signal is received by the probe and generates corresponding grayscale image data. In the process of real-time image acquisition, the ultrasound device can collect slice images of different angles in a continuous frame manner, combined with an efficient image processing algorithm to ensure that high-definition images of lesions inside the breast tissue are captured. In addition, the software system can control the scanning rate and probe angle to ensure the integrity of different tissue levels, and synchronize the scanning process to the image acquisition module in real time to ensure the consistency and accuracy of the image.

[0063] Extracting lesion detection information from image information for subsequent image processing can be achieved through a preset image analysis algorithm. First, the key features in ultrasound images can be extracted through edge detection algorithms, grayscale threshold segmentation, and region growing techniques. The software system will analyze the morphology, boundary features, and signal strength of the lesion area to screen out possible lesion areas. This information will be extracted as lesion detection information, including brightness distribution, boundary contours, echo intensity, texture features, geometric morphology and other data. These data are the basis of subsequent processing procedures and can provide a reference for further interference analysis and suppression. The software system can automatically mark the detected feature areas and generate a structured information file containing all these feature data for subsequent image processing procedures.

[0064] The purpose of this method is to effectively solve the echo interference problem caused by breast lesions close to blood vessels. By acquiring high-resolution image information in real time and extracting key lesion detection information from it, the system can identify possible interference areas at an early stage. Lesion detection information not only includes the morphological characteristics of the lesion, but also the signal intensity characteristics of the overlapping area with the vascular echo. This precise feature extraction provides basic data for the subsequent echo suppression mechanism, which can help the system effectively distinguish between interference and actual lesions. In this way, not only can the impact of echo interference on lesion identification be reduced, but also the matching accuracy under different lesion characteristics and interference conditions can be ensured, thereby improving the doctor's ability to judge the actual morphology and location of the lesion and avoiding misdiagnosis.

[0065] Acquiring echo interference analysis information from the extracted lesion detection information, and analyzing it after acquisition to determine whether the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference;

[0066] In this embodiment, echo interference analysis information is obtained from the extracted lesion detection information, and after being obtained, it is analyzed to determine whether the boundary and shape of the breast lesion will lead to inaccurate recognition due to echo interference, which specifically includes the following steps:

[0067] Acquiring echo interference analysis information from the extracted lesion detection information and performing preprocessing after acquisition;

[0068] To obtain echo interference analysis information from the extracted lesion detection information, the software can first be used to quantitatively analyze the signal strength, time delay, and spatial distribution in the lesion detection information. The specific implementation method includes using signal processing algorithms to classify the echo signals in the area around the lesion and extract the differences between the interference signals and the normal lesion signals. The echo interference features can be identified by detecting abnormal changes in frequency, phase, and waveform. In addition, the system will combine the analysis of the spatial domain and the time domain to extract the changes in the interference signals in different areas and at different time points, and generate complete echo interference analysis information, including the strength of the interference signal, time delay, and distribution pattern of the interference signal.

[0069] The purpose of preprocessing is to eliminate noise and unnecessary information interference in the signal, so that subsequent analysis is more accurate. Through software, preprocessing can include operations such as signal filtering, denoising, normalization, and data smoothing. First, signal filtering can use high-pass or low-pass filters to eliminate high-frequency or low-frequency noise interference to ensure that the echo interference information is clearer. Next, through denoising, the background noise that may exist in the detection information is removed to enhance the effective part of the signal. Normalization is used to standardize the values ​​between signals of different intensities to ensure the contrast and consistency of different signals. Data smoothing is used to reduce the drastic fluctuations in the signal, making the interference analysis results more stable. Through these steps, preprocessing can provide a more accurate and clear data basis for subsequent interference analysis.

[0070] Extracting echo signal fluctuation information and echo propagation anomaly information from the preprocessed echo interference analysis information, and analyzing them after extraction to generate interference signal intensity fluctuation coefficient and echo signal propagation anomaly index respectively;

[0071] Extracting echo signal fluctuation information can be achieved by analyzing the intensity changes of echo signals at multiple time points or frames. The specific method is to sample the echo signals at multiple time points or frames through software, and then calculate the difference in signal intensity between adjacent time points to capture the signal fluctuation. The software can automatically compare the data at each time point, identify the range and frequency of signal intensity changes, and thus generate echo signal fluctuation information. The extraction of echo propagation anomaly information is achieved by analyzing the difference between the actual signal propagation time and the theoretical predicted time. The software system calculates the theoretical signal propagation time based on the propagation speed of ultrasonic signals in different media, and compares it with the actual signal return time. The difference is the propagation anomaly information. This method can identify abnormal characteristics of echo signal propagation by quantitatively analyzing the delay or anomaly of the propagation time.

[0072] The generated interference signal intensity fluctuation coefficient and echo signal propagation abnormality index are used to construct an interference recognition model to generate an interference coefficient. The generated interference coefficient is compared with a pre-set interference coefficient threshold. Based on the comparison results, it is determined whether the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference.

[0073] The interference coefficient threshold can be determined through large-scale data analysis and historical case review. The software system first needs to collect actual data on the lesion area and the echo interference area from a large number of verified breast ultrasound images. Through machine learning algorithms or statistical models, these data are trained to analyze the relationship between lesion signals and interference signals, and identify the critical points where echo interference causes inaccurate recognition at different times and under different conditions. The system compares the calculated results of the interference coefficient with the actual lesion morphology manually annotated to find the coefficient range where signal interference has the most significant impact on lesion recognition accuracy. After multiple experiments and data verification, the software will generate a reasonable threshold range based on these analysis results as a pre-set interference coefficient threshold, which will be used to subsequently determine whether the degree of interference exceeds the allowable range of recognition error.

[0074] In this embodiment, the logic for obtaining the interference signal strength fluctuation coefficient and the echo signal propagation anomaly index is as follows:

[0075] Extract the echo signal fluctuation information from the preprocessed echo interference analysis information, specifically including the echo signal interference strength at different times within a period of time and the time interval between adjacent times, and calibrate the echo signal interference strength at different times within a period of time and the time interval between adjacent times as and , Indicates a period of time The echo signal interference strength at the time, , is a positive integer;

[0076] By real-time acquisition and analysis of echo signals in multiple frames of ultrasound images over a period of time, the interference strength of echo signals at different times and the time intervals between adjacent times can be extracted. The specific implementation method is to control the ultrasound device to collect signals multiple times within a predetermined time period, and calculate the signal strength at each time. After digital processing, the data is stored and the time stamp of each acquisition is recorded. , by calculating the time interval of adjacent timestamps These data reflect the fluctuation of interference through the change of echo signal strength. Signal strength It is derived from the echo energy when ultrasound propagates in tissues and is obtained through quantitative analysis of pixel grayscale or brightness using image processing algorithms.

[0077] Calculate the interference signal strength fluctuation coefficient. The specific calculation formula is as follows:

[0078]

[0079] In the formula, is the interference signal strength fluctuation coefficient;

[0080] The reason for using this formula to calculate the interference signal intensity fluctuation coefficient is that it can effectively capture the changes in the signal between different time points and evaluate the fluctuation degree of the interference signal by calculating the difference in signal intensity between adjacent time points. In ultrasound image analysis, the intensity of the interference signal may change unstably over time, especially when there is echo interference. This fluctuation may affect the accurate identification of the lesion. Therefore, by calculating the fluctuation coefficient of the interference signal intensity at each moment, the interference signal intensity at each moment can be effectively captured. and Signal strength at time point and Calculate the difference and get By averaging the sum of the squares of these differences, the impact of positive and negative fluctuations can be eliminated and the magnitude of the overall fluctuation can be reflected. Finally, divide by the time interval , is to standardize the fluctuation changes in different time periods to ensure that the impact of different time intervals on the volatility is balanced. In this way, by quantitatively analyzing the signal fluctuations in multiple time periods, the intensity fluctuation of the interference signal can be more accurately evaluated, thereby providing a data basis for subsequent interference identification and adjustment.

[0081] The magnitude of the interference signal intensity fluctuation coefficient directly reflects the degree of fluctuation of the interference signal in time, and this degree of fluctuation is closely related to whether the boundary and morphology of breast lesions are inaccurately identified due to echo interference. When the interference signal intensity fluctuation coefficient is large, it means that the intensity of the interference signal changes dramatically at different times, which means that the impact of echo interference on the boundary and morphology of the lesion is more significant, which may cause the actual morphology and boundary of the lesion to appear blurred or distorted in the ultrasound image, thereby affecting the accurate identification of the lesion location. On the contrary, if the interference signal intensity fluctuation coefficient is small, it means that the interference signal is relatively stable, has less impact on lesion identification, and the boundary and morphology of the lesion can be more accurately presented in the image. Therefore, by evaluating the magnitude of the interference signal intensity fluctuation coefficient, it is possible to effectively determine whether echo interference has a significant impact on the boundary and morphology identification of the lesion.

[0082] The echo propagation anomaly information in the preprocessed echo interference analysis information is extracted, including the actual propagation time of the echo signal, the theoretical propagation time, and the amplitude values ​​of the echo signal at different times during the propagation process, and calibrated as , and , represents the actual propagation time of the echo signal, represents the theoretical propagation time of the echo signal, Indicates the time period during which the echo signal propagates The amplitude value at the moment, , is a positive integer;

[0083] The signals collected by the ultrasound equipment are analyzed by software to extract the abnormal information of echo propagation. First, the actual propagation time can be obtained by measuring the time difference from the emission of ultrasound to the return of the echo. This time is accurately captured by the timing record of the ultrasound probe and receiver controlled by the software; the theoretical propagation time is calculated based on the sound wave propagation speed in the tissue and the known propagation distance. The propagation speed can refer to the typical acoustic characteristics of breast tissue. The amplitude value of the echo signal at different times during the propagation process is directly collected by the intensity of the received echo signal. The software quantifies the signal amplitude at each moment by digitally analyzing the grayscale value or brightness of the ultrasound image. Through continuous multiple acquisitions, combined with timestamps, the software can automatically extract the amplitude value at each moment, and synchronously record the actual propagation time and theoretical propagation time to form complete echo propagation abnormality information.

[0084] Calculate the echo signal propagation anomaly index. The specific calculation formula is as follows:

[0085]

[0086] In the formula, is the echo signal propagation anomaly index, It is the amplitude value of the echo signal at the initial moment during the propagation process.

[0087] The calculation formula of echo signal propagation anomaly index is to comprehensively evaluate the anomalies in the echo signal propagation process from multiple dimensions. The formula first considers the actual propagation time of the echo signal. and theoretical propagation time This reflects the deviation of the propagation time of the echo signal under ideal conditions and under actual conditions. , the square can eliminate the positive and negative differences, ensuring that the propagation time deviation is fully quantified, thereby reflecting the propagation anomalies of the echo signal in different tissue media. Next, the formula also adds the amplitude attenuation of the echo signal during the propagation process The signal amplitude value at each moment With initial amplitude The ratio of 2 and 3 is calculated and squared to measure the attenuation of the signal energy. The change in amplitude is directly related to the attenuation of the signal during propagation, reflecting the attenuation and loss that the signal may suffer during propagation. By summing the amplitude attenuation value at each moment , we can get the comprehensive impact of the energy loss of the signal during the entire propagation process. Finally, the formula is divided by the theoretical propagation time , which normalizes the propagation anomaly index relative to the theoretical propagation time of the signal, thereby ensuring comparability of signals with different propagation times. The purpose of the formula design is to comprehensively measure the propagation anomaly of the echo signal in terms of the time difference of signal propagation and energy attenuation, thereby providing a quantitative basis for identifying possible interference and errors.

[0088] The size of the echo signal propagation anomaly index directly reflects the time deviation and amplitude attenuation of the signal during propagation, which is closely related to whether the boundary and morphology of breast lesions are inaccurately identified due to echo interference. When the echo signal propagation anomaly index is large, it means that the actual propagation time of the signal deviates greatly from the theoretical propagation time, or the signal has a serious amplitude attenuation during the propagation process. These anomalies usually cause signal distortion, interfere with the clear presentation of the lesion area, and thus affect the accurate identification of the lesion boundary and morphology. The boundary may become blurred and the morphology may be distorted, resulting in identification errors. On the contrary, if the echo signal propagation anomaly index is small, it indicates that the signal propagation is more consistent with theoretical expectations, the signal attenuation is slight, and the boundary and morphology of the lesion can be presented more accurately. Therefore, by monitoring the size of the echo signal propagation anomaly index, it is possible to effectively determine whether echo interference causes errors in the identification of lesion boundaries and morphology.

[0089] In this embodiment, the generated interference signal strength fluctuation coefficient and echo signal propagation anomaly index Construct an interference recognition model and generate interference coefficients through weighted summation , and the generated interference coefficient The interference coefficient threshold is set Compare and judge whether the boundary and shape of breast lesions will be inaccurately identified due to echo interference based on the comparison results. The specific comparison analysis is as follows:

[0090] like , the boundaries and morphology of breast lesions will not be inaccurately identified due to echo interference, which means that echo interference has little impact on the boundaries and morphology of breast lesions, and the intensity fluctuation and propagation abnormality of the interference signal are within an acceptable range. In this case, the system can judge that the boundaries and morphology of the lesions in the ultrasound image are relatively accurate, and the actual location, size and contour of the lesions are basically consistent with the appearance in the image. The impact is that doctors can make more reliable diagnoses when analyzing these images, with a lower risk of misdiagnosis or inaccurate identification, and the follow-up tracking and treatment plan of the lesions can also proceed smoothly, and the reliability of data analysis is guaranteed;

[0091] like The boundaries and morphology of breast lesions can be inaccurately identified due to echo interference, indicating that echo interference has a significant impact on the boundaries and morphology of breast lesions. The abnormalities of echo signals during propagation and the fluctuations of interference signals exceed the expected range, resulting in blurred boundaries, distorted morphology or identification errors in lesions in ultrasound images, which makes it difficult for doctors to accurately judge the actual situation of the lesions and may lead to misdiagnosis or inability to accurately assess the progression of the lesions or the effectiveness of treatment. In addition, inaccurate identification of lesion morphology may also affect the formulation of treatment plans, which in turn has a negative impact on the patient's efficacy and recovery.

[0092] The generated interference signal strength fluctuation coefficient and echo signal propagation anomaly index The core of combining these two parameters to build an interference identification model is weighted summation. Specifically, the software performs weighted processing on these two parameters to generate a unified interference coefficient. , reflecting the overall interference situation of the lesion area. Weight coefficient and The setting of is based on the relative impact of different interference factors on recognition accuracy. Generally speaking, if the interference signal strength fluctuates greatly (i.e. Higher values ​​usually indicate that the boundaries and morphology of the lesion are more severely disturbed, so a larger weight can be assigned , in order to more keenly capture the impact of signal fluctuations on recognition; relatively speaking, if the signal propagation anomaly index It mainly reflects the time difference and amplitude attenuation in echo propagation. If its influence is relatively light, then The weight can be appropriately reduced. Through weighted summation, the final calculation formula is: ; This formula adjusts the weights of different interference factors to ensure that the interference identification model can fully consider the combined impact of signal fluctuations and propagation anomalies, thereby generating an accurate interference coefficient , which is used to determine the overall impact of echo interference on lesion identification. The weight setting can be obtained by analyzing a large amount of historical data to ensure that its impact on lesion identification accuracy is maximized.

[0093] According to the judgment results, an echo suppression mechanism is constructed to suppress the influence of echo interference on lesion identification, and after construction, it is applied to the image processing process;

[0094] In this embodiment, based on the judgment result, an echo suppression mechanism for suppressing the influence of echo interference on lesion identification is constructed, and after construction, it is applied to the image processing process, specifically including: when the judgment result is that the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference, by analyzing the intensity and propagation characteristics of the echo signal, the spatiotemporal distribution characteristics of the interference signal are extracted, and an echo suppression mechanism is constructed based on these characteristics; the mechanism includes frequency domain separation of the interference signal, signal intensity normalization processing, and a signal suppression strategy based on time and space distribution characteristics; by distinguishing and processing the interference signal and the effective signal, the influence of the interference signal on the image generation process is reduced; the constructed echo suppression mechanism is applied to the image processing process, and an ultrasound image with suppressed interference is generated through the signal processed data, and the mechanism is continuously applied in the image processing process to optimize the recognition results.

[0095] The software can automatically analyze the spatiotemporal characteristics of the echo signal to extract the interference signal. The specific method is to use signal processing methods to analyze the propagation of ultrasound in different tissues in combination with the two dimensions of time and space. First, the ultrasound device will collect the intensity data of the echo signal in real time. The software generates a signal intensity graph based on the collected intensity information, and compares it with the theoretical propagation model to identify abnormal points of the signal. In addition, by analyzing the signal propagation delay and intensity attenuation at different times, the spatiotemporal distribution characteristics of the interference signal are extracted. This is done to identify the distribution of interference signals in different spaces and times, so as to ensure that the interference signals can be accurately located and processed in subsequent processing, thereby improving the accuracy of lesion identification.

[0096] In terms of frequency domain separation, the software can use fast Fourier transform (FFT) or other frequency analysis methods to convert time domain signals into frequency domain signals, extract the frequency characteristics of interference signals, and distinguish them from lesion signals. This is because different signals (such as interference signals and effective signals) often behave differently in frequency. Signal intensity normalization processing can eliminate the difference in signal intensity in different time or space by standardizing the signal intensity, making it easier to distinguish interference signals from effective signals in subsequent processing. The signal suppression strategy based on time and space distribution characteristics is to weaken or filter out interference signals in specific areas through software, especially for areas where interference signals are concentrated and recurring. Doing so can reduce the impact of interference signals on image processing and ensure the accuracy of lesion identification.

[0097] The key to distinguishing interference signals from valid signals is to use the previously extracted spatiotemporal distribution characteristics and frequency information. The software can detect the characteristic patterns of interference signals (such as abnormal signal intensity fluctuations, propagation time delays, etc.) through pattern recognition methods and separate them from valid signals. This can be achieved through filters or signal suppression algorithms, which only retain valid signals and remove or weaken interference signals when generating images. In this way, the interference of echo interference on lesion identification in ultrasound images can be significantly reduced, and a clearer and more accurate ultrasound image can be generated to ensure that the boundaries and morphology of the lesions can be accurately presented.

[0098] The application of echo suppression mechanism in image processing can be achieved by directly inputting the processed signal into the image generation step. Specifically, the software converts the effective signal from which the interference signal has been removed into pixel information to generate an ultrasound image with interference suppressed. In the subsequent image processing process, the software continues to monitor the signal quality and applies the suppression mechanism in real time to ensure that the image generation process will not be affected by new interference signals. By continuously applying the suppression mechanism, the system can maintain the suppression of interference signals when each frame of the image is generated, ensuring that the lesion identification results throughout the entire processing flow are always stable and accurate. Doing so can greatly improve image quality and enable doctors to make diagnoses based on these images more reliably.

[0099] During the application of the echo suppression mechanism, the echo suppression feedback information is obtained in real time and analyzed after acquisition to evaluate whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and the echo suppression mechanism is adjusted according to the evaluation results;

[0100] In this embodiment, during the application of the echo suppression mechanism, echo suppression feedback information is obtained in real time, and analyzed after acquisition to evaluate whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and the echo suppression mechanism is adjusted according to the evaluation results, which specifically includes the following steps:

[0101] During the application of the echo suppression mechanism, the echo suppression feedback information is obtained in real time and preprocessed after being obtained;

[0102] During the application of the echo suppression mechanism, the echo suppression feedback information can be obtained through the signal data generated in the real-time acquisition system. These data usually include the strength, frequency and phase change of the interference signal. The software can extract these data from the device at fixed time intervals by controlling the signal acquisition module of the ultrasound device, and record the signal characteristics at each moment to form the echo suppression feedback information. This information is usually stored in a digital form, including: 1) the real-time strength of the interference signal, 2) the frequency distribution of the interference signal, and 3) the phase change of the interference signal. These three types of data together constitute the complete echo suppression feedback information for subsequent analysis.

[0103] The main purpose of preprocessing is to remove noise and anomalies in the data to ensure that subsequent analysis can be based on high-quality feedback information. The preprocessing steps may include: 1) Data filtering, which is used to remove high-frequency or low-frequency noise introduced by signal equipment or the external environment to ensure the stability of signal strength, frequency, and phase data; 2) Anomaly detection and removal, by analyzing the change trend of the signal, identifying and removing abnormal signal strength or phase values ​​that do not meet the normal range; 3) Data standardization, normalizing the signal strength and frequency to ensure that data in different time periods are comparable. Through these preprocessing steps, the signal data will be optimized to ensure more accurate analysis of the effect of the echo suppression mechanism.

[0104] Extracting comprehensive information of echo signal characteristics from the preprocessed echo suppression feedback information, and analyzing it after extraction to generate echo signal strength attenuation coefficient and signal strength fluctuation mean square coefficient respectively;

[0105] In the pre-processed echo suppression feedback information, the software can automatically extract three types of core data through feature extraction algorithms: interference signal strength, frequency, and phase. The specific implementation method is that first, the software will classify the pre-processed data and independently extract the signal strength, frequency, and phase values ​​at each time point. Then, the software will filter the effective signal data according to the set feature threshold, for example: for intensity data, extract the intensity change segment with large fluctuations; for frequency data, extract the frequency range with abnormal changes; for phase data, extract the part with phase jump or distortion. In this way, the system can identify the key feature data in the signal and form "echo signal feature comprehensive information" for subsequent analysis and suppression effect evaluation. The extraction process of these feature information is completely automated by software, thereby ensuring the real-time and accuracy of data extraction.

[0106] The generated echo signal intensity attenuation coefficient and signal intensity fluctuation mean square coefficient are used to construct a suppression effect model to generate a suppression coefficient. The generated suppression coefficient is compared with the pre-set suppression coefficient threshold. Based on the comparison results, it is evaluated whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and the echo suppression mechanism is adjusted based on the evaluation results.

[0107] The preset suppression coefficient threshold is usually determined by combining historical data analysis with expert knowledge. First, the software collects and analyzes a large amount of historical ultrasound image data and its corresponding echo suppression results, including different types of echo interference and their suppression effects. Based on these historical data, the software uses statistical analysis techniques, such as mean, standard deviation, etc., to calculate the distribution characteristics of the suppression coefficient and extract representative suppression coefficient values ​​from them. Experts can adjust the results on this basis and set a suitable threshold based on actual medical needs and interference conditions. The specific methods include: calculating the percentile of the effective suppression coefficient in the historical data (such as the 90th percentile) as the preliminary threshold; combining the requirements of the actual application scenario, adjusting the threshold through iterative optimization to ensure that it can effectively distinguish different interference levels. Finally, this preset threshold will be input into the system and used for evaluation and comparison in real-time data processing to ensure the effectiveness and reliability of the echo suppression mechanism in practical applications.

[0108] In this embodiment, the logic for obtaining the echo signal strength attenuation coefficient and the signal strength fluctuation mean square coefficient is as follows:

[0109] Extract the comprehensive information of echo signal characteristics in the preprocessed echo suppression feedback information, including the strength, frequency, phase and corresponding time points of the interference signal at different times during the application of the echo suppression mechanism, and use the function to calculate the strength, frequency and phase of the interference signal at different times during the application of the echo suppression mechanism according to the time series. , and To express, For time point, Indicates the period of time during which the echo suppression mechanism is applied. The strength of the interference signal at all times, Indicates the period of time during which the echo suppression mechanism is applied. The frequency of the interference signal at all times, Indicates the period of time during which the echo suppression mechanism is applied. The phase of the interference signal at each moment, and the time period is defined as ;

[0110] During the application of the echo suppression mechanism, the software can obtain the intensity, frequency and phase data of the echo signal from the ultrasonic device at regular intervals through the preset acquisition module, and record the time points corresponding to these data. The specific implementation method is that the software monitors the acquisition points of the signal in real time and obtains the intensity, frequency and phase of the signal at each acquisition moment. These data are captured by the sensor in the ultrasonic device and are preliminarily filtered and denoised by the signal processing module to ensure that the acquired signal information is accurate and stable. These three types of data come from: 1) Intensity represents the amplitude change of the signal at a certain point in time, reflecting the energy of the signal; 2) Frequency is the speed of signal fluctuation, indicating the vibration frequency of the signal at each time point; 3) Phase is the phase position of the signal wave, which can reflect the propagation delay and waveform distortion of the signal. The software collects and stores these data at each preset time point to provide a basis for subsequent suppression effect evaluation.

[0111] Calculate the echo signal strength attenuation coefficient. The specific calculation formula is as follows:

[0112]

[0113] In the formula, is the echo signal strength attenuation coefficient, is the strength of the interference signal at the initial moment during the application of the echo suppression mechanism. is the frequency of the interfering signal at the initial moment during a period of time during the application of the echo suppression mechanism;

[0114] The calculation formula of the echo signal strength attenuation coefficient evaluates the signal attenuation by comparing the initial signal strength and frequency with the changes at subsequent time points. In the formula, Represents the product of signal strength and frequency at the initial moment, which serves as the reference value; Represents a point in time The formula captures the signal attenuation over time by calculating the difference between the initial value and the current value. The integral is used to accumulate the signal changes over the entire time period, thus reflecting the signal attenuation trend throughout the process. The product of the initial signal strength and frequency in the denominator is used for normalization to ensure consistency in the comparison results of different signals. This calculation method can fully evaluate the overall attenuation effect of the echo suppression mechanism on the signal.

[0115] Echo signal strength attenuation coefficient The size of directly reflects the intensity reduction degree of the signal under the action of the echo suppression mechanism, and its size is closely related to the evaluation of the suppression effect of the echo suppression mechanism. When the value is large, it means that the signal strength is significantly attenuated under the action of the suppression mechanism, indicating that the influence of the interference signal is effectively suppressed, making it easier to distinguish between breast lesions and echo interference areas. In this case, the boundaries and morphology of breast lesions can be more clearly highlighted from the interference signal, and the suppression effect is ideal; The smaller the value, the more effective the suppression mechanism is in weakening the interference signal. The interference signal is still strong, which may confuse the lesion area with the interference area, resulting in the inability to accurately identify the boundary and morphology of the lesion. The larger the value, the better the suppression effect, which can more clearly distinguish the lesion from the interference area. A value that is too small indicates that the interference suppression is insufficient and it is difficult to effectively distinguish the two.

[0116] Calculate the mean square coefficient of signal strength fluctuation. The specific calculation formula is as follows:

[0117]

[0118] In the formula, is the mean square coefficient of signal intensity fluctuation, It indicates the rate of change of the intensity of the interference signal at different times over a period of time during the application of the echo suppression mechanism. It indicates the rate of change of the phase of the interference signal at different times over a period of time during the application of the echo suppression mechanism.

[0119] This formula is used to calculate the mean square coefficient of signal strength fluctuation. The purpose is to evaluate the fluctuation of signal strength and phase over time to determine the smoothing effect of the echo suppression mechanism on signal fluctuations. is the normalization process, which is used to standardize the fluctuations over the entire time period. The integral part is expressed in arrive The signal strength change rate in the time interval and the phase change rate The sum of the squares of the sum of squares is accumulated to reflect the degree of fluctuation at different time points. Through the square sum operation, the part with larger fluctuation can be amplified, so that the moment with large fluctuation amplitude has a greater impact on the overall fluctuation coefficient, thereby more accurately evaluating the smoothing effect of the signal under the action of the echo suppression mechanism. If the fluctuation is large, If the value is large, it indicates that the signal is unstable; if the value is small, it means that the signal fluctuates less and the echo suppression mechanism works well.

[0120] Signal strength fluctuation mean square coefficient The size of reflects the fluctuation stability of the signal under the action of the echo suppression mechanism, and its size is directly related to whether the suppression effect can meet the requirements of distinguishing breast lesions from echo interference areas. A smaller value indicates that the signal intensity and phase fluctuate less over time and the signal is more stable, which means that the echo suppression mechanism can effectively smooth the signal and reduce unnecessary interference fluctuations, making the boundaries and morphology of breast lesions clearer in ultrasound images. When the value is large, it means that the signal intensity and phase fluctuate frequently and violently, and the echo suppression mechanism fails to effectively smooth the interference signal, resulting in unstable signals in the ultrasound image and difficulty in distinguishing between the lesion area and the interference area. The larger the value, the greater the signal fluctuation, the worse the suppression effect, and the more significant the interference in the image; A smaller value indicates a better suppression effect, a more stable signal fluctuation, and an easier and more accurate distinction between the lesion and the echo interference area. A value of 0 indicates that the echo suppression mechanism can better meet the requirement of distinguishing lesions from interference areas.

[0121] In this embodiment, the generated echo signal strength attenuation coefficient and signal strength fluctuation mean square coefficient Construct an inhibition effect model and generate an inhibition coefficient by weighted summation , and the resulting suppression coefficient With the preset suppression coefficient threshold A comparison is performed to evaluate whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas based on the comparison results, and the echo suppression mechanism is adjusted based on the evaluation results. The specific comparison analysis is as follows:

[0122] like , the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and there is no need to adjust the echo suppression mechanism;

[0123] This situation means that the echo suppression mechanism works well, the interference signal has been effectively suppressed, and the boundaries and morphology of the breast lesions can be clearly presented in the ultrasound image without being affected by echo interference. In this case, the intensity, frequency, and phase fluctuations of the echo signal are already within a reasonable range, indicating that the existing suppression mechanism can well distinguish the lesion area from the echo interference area. Therefore, there is no need to further adjust the echo suppression mechanism, and the current mechanism can be directly applied to subsequent processing to ensure that the system can continue to provide clear, interference-free lesion image data, providing an accurate basis for doctors' diagnosis and treatment.

[0124] like The suppression effect of the echo suppression mechanism cannot meet the requirements of distinguishing breast lesions from echo interference areas, and the echo suppression mechanism needs to be adjusted, including: by analyzing the intensity, frequency and phase changes of the current echo signal, readjusting the filtering parameters of the echo signal, optimizing the frequency domain separation algorithm to further eliminate frequency interference, and at the same time, for the interval with large signal intensity fluctuations, enhancing the intensity normalization processing, reducing the signal fluctuation amplitude, and recalibrating the interference suppression strategy in combination with the distribution characteristics of time and space.

[0125] This situation shows that the current echo suppression mechanism cannot effectively distinguish breast lesions from echo interference areas, and the interference signal is still strong, resulting in unclear lesion boundaries and morphology, which may mislead the doctor's diagnosis. In this case, adjustments need to be made in a variety of ways. First, the software can be used to analyze the fluctuations in the intensity, frequency, and phase of the current signal, strengthen the filtering processing for the parts with large fluctuations, optimize the frequency domain separation algorithm, and reduce the interference signals on the frequency. Secondly, enhance the normalization processing of signal intensity, reduce the amplitude of intensity fluctuations, and ensure the stability of the signal at different time points. Finally, by re-adjusting the interference suppression strategy and combining the time and space distribution characteristics, the interference signal can be more accurately separated and suppressed. In these ways, the effect of the echo suppression mechanism can be further improved to meet the requirements of distinguishing lesions from interference areas.

[0126] In order to construct a suppression effect model and generate a suppression coefficient by weighted summation, the echo signal strength attenuation coefficient and signal strength fluctuation mean square coefficient Combined for processing. The larger the value, the better the inhibition effect. The smaller the value, the better the inhibition effect. Therefore, when constructing the inhibition effect model, should be given a higher positive weight. A higher negative weight should be given. The specific method is: the software sets an appropriate weight coefficient based on experimental and historical data, such as and , and these two coefficients are respectively and The final suppression coefficient is The calculation formula is: Here, the weight coefficient The weight coefficient can be set to a larger positive number by analyzing the impact of the echo signal strength weakening on the suppression effect. The negative impact of signal fluctuations on image stability is set to a larger positive number. In this way, the suppression coefficient The size of can accurately reflect the effect of the inhibition mechanism. The higher the inhibition coefficient, the better the inhibition effect, which can effectively distinguish the lesion from the interference area.

[0127] Lesion matching is completed according to the adjusted echo suppression mechanism, and the results of lesion matching and the application process of the echo suppression mechanism are recorded and stored for subsequent monitoring of lesion changes and further analysis and optimization.

[0128] Implementation of lesion matching: After applying the adjusted echo suppression mechanism, the software can re-analyze and match the lesion boundaries and morphological features in the ultrasound image. The specific implementation method is that the software will identify the edges of the lesions based on the suppressed image signals, and use the pattern matching algorithm to compare the morphology of the lesions with the previous reference images. By extracting quantitative features such as the geometric shape, boundary position, and brightness information of the lesion area, the lesions are accurately matched after being clearly distinguished from the echo interference area. The matching results of the lesions will be generated in a digital form to ensure that the location information, size, and morphological characteristics of the lesions can be clearly stored and used for subsequent monitoring.

[0129] Implementation of recording and storage mechanism: After the matching is completed, the software will automatically record the results of lesion matching and the detailed application process of the inhibition mechanism. The recorded content includes the echo signal intensity attenuation coefficient, the signal intensity fluctuation mean square coefficient, the evaluation results of the inhibition effect, and the final lesion matching morphology, position and other data. These data will be stored in an orderly manner through the built-in database module of the software. The structured storage of the data will facilitate subsequent query and analysis. At the same time, the storage process will be accompanied by a timestamp to ensure that time comparison can be made in the subsequent lesion change analysis. This method can not only provide doctors with a detailed image processing process, but also provide a reference for future lesion changes and support the development trend analysis of lesions.

[0130] Why do this and why it is important: The main purpose of recording and storing the results of lesion matching and the application process of the inhibition mechanism is to ensure that there is sufficient historical data support for future lesion change monitoring. By recording the process of each lesion matching, doctors can compare the changes in lesions at different time points, such as whether the morphology has changed, whether the edges are clear, whether the effect of the inhibition mechanism has improved, etc. In addition, these data can also be used to further optimize the inhibition mechanism. By analyzing the changing trends of different parameters, the software can provide more personalized and dynamic interference suppression strategies. Especially in the long-term follow-up of patient treatment, this recording and storage mechanism helps doctors make more accurate judgments and decisions based on historical data.

[0131] like Figure 2The breast lesion matching system based on ultrasound images shown includes an ultrasound image real-time acquisition module, an echo interference analysis module, an echo suppression construction module, an echo suppression feedback evaluation module, and a lesion matching and data storage module;

[0132] Ultrasonic image real-time acquisition module, which scans breast tissue through ultrasonic equipment, obtains image information of breast lesions in real time, and extracts lesion detection information for subsequent image processing flow from the image information;

[0133] The echo interference analysis module obtains the echo interference analysis information from the extracted lesion detection information, and performs analysis after obtaining it to determine whether the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference;

[0134] An echo suppression construction module constructs an echo suppression mechanism for suppressing the influence of echo interference on lesion identification according to the judgment result, and applies it to the image processing flow after construction;

[0135] The echo suppression feedback evaluation module obtains echo suppression feedback information in real time during the application of the echo suppression mechanism, analyzes it after acquisition, evaluates whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and adjusts the echo suppression mechanism according to the evaluation results;

[0136] The lesion matching and data storage module completes lesion matching according to the adjusted echo suppression mechanism, and records and stores the results of lesion matching and the application process of the echo suppression mechanism for subsequent monitoring of lesion changes and further analysis and optimization.

[0137] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0138] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0139] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0140] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0141] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0144] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A breast lesion matching method based on ultrasound images, characterized in that: The specific steps include: Scan breast tissue with ultrasound equipment to obtain image information of breast lesions in real time, and extract lesion detection information from the image information for subsequent image processing procedures; Acquiring echo interference analysis information from the extracted lesion detection information, and analyzing it after acquisition to determine whether the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference; According to the judgment results, an echo suppression mechanism is constructed to suppress the influence of echo interference on lesion identification, and after construction, it is applied to the image processing process; According to the judgment result, an echo suppression mechanism for suppressing the influence of echo interference on lesion identification is constructed, and after construction, it is applied to the image processing process, specifically including: when the judgment result is that the boundary and shape of the breast lesion will lead to inaccurate identification due to echo interference, by analyzing the intensity and propagation characteristics of the echo signal, the temporal and spatial distribution characteristics of the interference signal are extracted, and an echo suppression mechanism is constructed based on these characteristics; the mechanism includes frequency domain separation of the interference signal, signal intensity normalization processing and signal suppression strategy based on time and space distribution characteristics; by distinguishing and processing the interference signal and the effective signal, the influence of the interference signal on the image generation process is reduced; the constructed echo suppression mechanism is applied to the image processing process, and the ultrasound image with suppressed interference is generated through the signal processed data, and the mechanism is continuously applied in the image processing process to optimize the recognition result; During the application of the echo suppression mechanism, the echo suppression feedback information is obtained in real time and analyzed after acquisition to evaluate whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and the echo suppression mechanism is adjusted according to the evaluation results; During the application of the echo suppression mechanism, the echo suppression feedback information is obtained in real time and analyzed after being obtained to evaluate whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and the echo suppression mechanism is adjusted according to the evaluation results, which specifically includes the following steps: During the application of the echo suppression mechanism, the echo suppression feedback information is obtained in real time and preprocessed after being obtained; Extracting comprehensive information of echo signal characteristics from the preprocessed echo suppression feedback information, and analyzing it after extraction to generate echo signal strength attenuation coefficient and signal strength fluctuation mean square coefficient respectively; The generated echo signal intensity attenuation coefficient and signal intensity fluctuation mean square coefficient are used to construct a suppression effect model, a suppression coefficient is generated, and the generated suppression coefficient is compared with a preset suppression coefficient threshold value, and whether the suppression effect of the echo suppression mechanism can meet the requirement of distinguishing breast lesions from echo interference areas is evaluated according to the comparison result, and the echo suppression mechanism is adjusted according to the evaluation result; Lesion matching is completed according to the adjusted echo suppression mechanism, and the results of lesion matching and the application process of the echo suppression mechanism are recorded and stored for subsequent monitoring of lesion changes and further analysis and optimization.

2. The method for matching breast lesions based on ultrasound images according to claim 1, characterized in that: Acquiring echo interference analysis information from the extracted lesion detection information, and analyzing it after acquisition to determine whether the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference, specifically including the following steps: Acquiring echo interference analysis information from the extracted lesion detection information and performing preprocessing after acquisition; Extracting echo signal fluctuation information and echo propagation anomaly information from the preprocessed echo interference analysis information, and analyzing them after extraction to generate interference signal intensity fluctuation coefficient and echo signal propagation anomaly index respectively; The generated interference signal intensity fluctuation coefficient and echo signal propagation abnormality index are used to construct an interference recognition model to generate an interference coefficient. The generated interference coefficient is compared with a pre-set interference coefficient threshold. Based on the comparison results, it is determined whether the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference.

3. The method for matching breast lesions based on ultrasound images according to claim 2, characterized in that: The logic for obtaining the interference signal intensity fluctuation coefficient and the echo signal propagation anomaly index is as follows: Extract the echo signal fluctuation information from the preprocessed echo interference analysis information, specifically including the echo signal interference strength at different times within a period of time and the time interval between adjacent times, and calibrate the echo signal interference strength at different times within a period of time and the time interval between adjacent times as and , Indicates a period of time The echo signal interference strength at the time, , is a positive integer; Calculate the interference signal strength fluctuation coefficient. The specific calculation formula is as follows: In the formula, is the interference signal strength fluctuation coefficient; The echo propagation anomaly information in the preprocessed echo interference analysis information is extracted, including the actual propagation time of the echo signal, the theoretical propagation time, and the amplitude values ​​of the echo signal at different times during the propagation process, and calibrated as , and , represents the actual propagation time of the echo signal, represents the theoretical propagation time of the echo signal, Indicates the time period during which the echo signal propagates The amplitude value at the moment, , is a positive integer; Calculate the echo signal propagation anomaly index. The specific calculation formula is as follows: In the formula, is the echo signal propagation anomaly index, It is the amplitude value of the echo signal at the initial moment during the propagation process.

4. The method for matching breast lesions based on ultrasound images according to claim 3, characterized in that: The generated interference signal strength fluctuation coefficient and echo signal propagation anomaly index Construct an interference recognition model and generate interference coefficients through weighted summation , and the generated interference coefficient The interference coefficient threshold is set Compare and judge whether the boundary and shape of breast lesions will be inaccurately identified due to echo interference based on the comparison results. The specific comparison analysis is as follows: like , the boundaries and morphology of breast lesions will not be inaccurately identified due to echo interference; like , the boundaries and morphology of breast lesions can lead to inaccurate identification due to echo interference.

5. The method for matching breast lesions based on ultrasound images according to claim 4, characterized in that: The logic for obtaining the echo signal strength attenuation coefficient and the signal strength fluctuation mean square coefficient is as follows: Extract the comprehensive information of echo signal characteristics in the preprocessed echo suppression feedback information, including the strength, frequency, phase and corresponding time points of the interference signal at different times during the application of the echo suppression mechanism, and use the function to calculate the strength, frequency and phase of the interference signal at different times during the application of the echo suppression mechanism according to the time series. , and To express, For time point, Indicates the period of time during which the echo suppression mechanism is applied. The strength of the interference signal at all times, Indicates the period of time during which the echo suppression mechanism is applied. The frequency of the interference signal at all times, Indicates the period of time during which the echo suppression mechanism is applied. The phase of the interference signal at each moment, and the time period is defined as ; Calculate the echo signal strength attenuation coefficient. The specific calculation formula is as follows: In the formula, is the echo signal strength attenuation coefficient, is the strength of the interference signal at the initial moment during the application of the echo suppression mechanism. is the frequency of the interfering signal at the initial moment during a period of time during the application of the echo suppression mechanism; Calculate the mean square coefficient of signal strength fluctuation. The specific calculation formula is as follows: In the formula, is the mean square coefficient of signal intensity fluctuation, It indicates the rate of change of the intensity of the interference signal at different times over a period of time during the application of the echo suppression mechanism. It indicates the rate of change of the phase of the interference signal at different times over a period of time during the application of the echo suppression mechanism.

6. The method for matching breast lesions based on ultrasound images according to claim 5, characterized in that: The generated echo signal strength attenuation coefficient and signal strength fluctuation mean square coefficient Construct an inhibition effect model and generate an inhibition coefficient by weighted summation , and the resulting suppression coefficient With the preset suppression coefficient threshold A comparison is performed to evaluate whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas based on the comparison results, and the echo suppression mechanism is adjusted based on the evaluation results. The specific comparison analysis is as follows: like , the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and there is no need to adjust the echo suppression mechanism; like The suppression effect of the echo suppression mechanism cannot meet the requirements of distinguishing breast lesions from echo interference areas, and the echo suppression mechanism needs to be adjusted, including: by analyzing the intensity, frequency and phase changes of the current echo signal, readjusting the filtering parameters of the echo signal, optimizing the frequency domain separation algorithm to further eliminate frequency interference, and at the same time, for the interval with large signal intensity fluctuations, enhancing the intensity normalization processing, reducing the signal fluctuation amplitude, and recalibrating the interference suppression strategy in combination with the distribution characteristics of time and space.

7. A breast lesion matching system based on ultrasound images, used to implement a breast lesion matching method based on ultrasound images as described in any one of claims 1 to 6, characterized in that: It includes an ultrasound image real-time acquisition module, an echo interference analysis module, an echo suppression construction module, an echo suppression feedback evaluation module, and a lesion matching and data storage module; Ultrasonic image real-time acquisition module, which scans breast tissue through ultrasonic equipment, obtains image information of breast lesions in real time, and extracts lesion detection information for subsequent image processing flow from the image information; The echo interference analysis module obtains the echo interference analysis information from the extracted lesion detection information, and performs analysis after obtaining it to determine whether the boundary and morphology of the breast lesion will lead to inaccurate identification due to echo interference; An echo suppression construction module constructs an echo suppression mechanism for suppressing the influence of echo interference on lesion identification according to the judgment result, and applies it to the image processing flow after construction; The echo suppression feedback evaluation module obtains echo suppression feedback information in real time during the application of the echo suppression mechanism, analyzes it after acquisition, evaluates whether the suppression effect of the echo suppression mechanism can meet the requirements of distinguishing breast lesions from echo interference areas, and adjusts the echo suppression mechanism according to the evaluation results; The lesion matching and data storage module completes lesion matching according to the adjusted echo suppression mechanism, and records and stores the results of lesion matching and the application process of the echo suppression mechanism for subsequent monitoring of lesion changes and further analysis and optimization.

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