Ultrasonic endoscope image processing method and device

Abstract

The invention provides an ultrasonic endoscope image processing method and equipment, is applied to the technical field of image processing, and is used for artifact detection and gain adjustment by acquiring ultrasonic echo signals and performing frequency domain analysis and extracting low-frequency-band energy information. A low-frequency-band energy baseline value is preset, then an artifact detection dynamic threshold value is calculated according to the low-frequency-band energy baseline value and an energy fluctuation range, a low-frequency gain suppression factor is calculated according to a low-frequency-band energy change rate, and the low-frequency-band energy change rate reflects the occurrence speed of artifacts and is used for dynamically adjusting gain suppression intensity. And finally, applying the real-time low-frequency gain coefficient to the ultrasonic echo signal of the frequency domain, performing gain adjustment, converting the adjusted frequency domain signal into a time domain signal, and performing reconstruction to generate an ultrasonic endoscope image, thereby realizing the effect of improving the quality of the ultrasonic endoscope image.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to an ultrasonic endoscopic image processing method and device. Background Art

[0002] In minimally invasive endoscopic ultrasound surgery, endoscopists use ultrasound endoscopes to diagnose and treat in human cavities. Doctors guide endoscopic operations and identify lesions by observing ultrasound images. However, when the endoscope passes through vascular-rich areas or high-density tissues, the quality of ultrasound images is easily reduced. Bleeding will produce artifacts, and tissue attenuation will weaken the ultrasound signal. The combination of these factors makes traditional image processing methods limited in improving image quality in complex situations. The deterioration of image quality makes it difficult for doctors to accurately judge the location and nature of lesions, which directly affects surgical efficiency and patient safety. Therefore, under complex physiological conditions, it is crucial to provide high-quality ultrasound images to meet real-time diagnostic needs. Existing image processing technologies, such as fixed gain compensation or linear filtering, are difficult to effectively deal with dynamic image degradation caused by complex physiological environments, especially when bleeding artifacts and tissue attenuation occur at the same time. The image quality improvement effect is poor and cannot fully meet the dual needs of minimally invasive surgery for real-time performance and image quality.

[0003] In minimally invasive endoscopic ultrasound surgery under complex physiological environments, the real-time degradation of ultrasound image quality caused by the superposition of bleeding artifacts and tissue attenuation is a key issue restricting diagnostic accuracy and surgical efficiency. Existing image processing technology is difficult to effectively cope with the dynamic changes of the physiological environment and cannot provide continuous high-quality image support. Therefore, how to design a real-time ultrasound image processing method that can adapt to the dynamic changes of complex physiological environments and effectively suppress artifacts and compensate for attenuation without adding additional equipment burden and operational complexity has become a problem that needs to be solved urgently. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present application provides an ultrasonic endoscopic image processing method and device, which are applied in the field of image processing technology and have the advantage of improving the quality of ultrasonic endoscopic images.

[0005] In a first aspect, an endoscopic ultrasound image processing method is provided for improving the quality of ultrasound images in minimally invasive endoscopic ultrasound surgery, the method comprising the steps of: S1: Acquire an ultrasonic echo signal, perform frequency domain analysis, and extract low-frequency energy information, wherein the low-frequency energy information at least includes: a low-frequency energy value, a low-frequency energy fluctuation range, and a low-frequency energy change rate; S2: Obtaining a preset low-frequency band energy baseline value; S3: calculating a dynamic threshold for artifact detection according to the low-frequency band energy baseline value and the low-frequency band energy fluctuation range; S4: Calculating a low-frequency gain suppression factor according to the low-frequency band energy change rate; S5: Calculating a real-time low-frequency gain coefficient according to the artifact detection dynamic threshold, the low-frequency gain suppression factor and the low-frequency energy value; S6: Apply the real-time low-frequency gain coefficient to the ultrasound echo signal in the frequency domain to perform gain adjustment, convert the gain-adjusted frequency domain signal into a time domain signal, perform image reconstruction, and generate an ultrasound endoscopic image.

[0006] The present application provides an ultrasonic endoscopy processing method, which obtains ultrasonic echo signals and performs frequency domain analysis to extract low-frequency energy information, including energy value, energy fluctuation range and energy change rate, which are used for subsequent artifact detection and gain adjustment. The scheme pre-sets the low-frequency energy baseline value as an energy reference under normal circumstances. Then, the dynamic threshold for artifact detection is calculated based on the low-frequency energy baseline value and the energy fluctuation range. This dynamic threshold for artifact detection can adapt to signal fluctuations and detect artifacts more accurately. The scheme also calculates the low-frequency gain suppression factor based on the low-frequency energy change rate. The low-frequency energy change rate reflects the speed of the artifact appearance and is used to dynamically adjust the gain suppression intensity. The scheme combines the dynamic threshold for artifact detection, the low-frequency gain suppression factor and the current low-frequency energy value to calculate the real-time low-frequency gain coefficient and realize dynamic adjustment of the gain. Finally, the scheme applies the real-time low-frequency gain coefficient to the ultrasonic echo signal in the frequency domain, performs gain adjustment, and converts the adjusted frequency domain signal into a time domain signal to reconstruct and generate an ultrasonic endoscopy image, ultimately achieving the effect of improving the quality of the ultrasonic endoscopy image. Therefore, this scheme realizes dynamic gain adjustment of ultrasound images by analyzing and utilizing the low-frequency energy information of ultrasound echo signals, thereby effectively improving the quality of ultrasound images in minimally invasive ultrasound endoscopic surgery.

[0007] Further, step S3 includes: S31: Acquire a coupling parameter between the ultrasonic endoscopic probe and the lesion area, wherein the coupling parameter at least includes an ultrasonic echo signal intensity; S32: Calculating a coupling degree confidence factor according to the ultrasonic echo signal strength; S33: Calculate the artifact detection dynamic threshold according to the coupling degree confidence factor, the low-frequency band energy baseline value, and the low-frequency band energy fluctuation range.

[0008] The present application provides an ultrasonic endoscopic processing method, which calculates a coupling confidence factor based on the ultrasonic echo signal intensity to quantify the coupling parameter into a coupling confidence factor, and incorporates the coupling confidence factor into the calculation of a dynamic threshold, so that the dynamic threshold not only depends on the low-frequency band energy information, but is also related to the probe coupling state, thereby improving the accuracy and reliability of the dynamic threshold for artifact detection, thereby improving the performance of the ultrasonic endoscopic image processing method.

[0009] Further, in step S33, the formula for calculating the dynamic threshold of artifact detection according to the coupling degree confidence factor, the low-frequency band energy baseline value, and the low-frequency band energy fluctuation range is: DT = BLV * (1 + α * FR / BLV) * exp(-β * (1 - CCF)); wherein DT is the dynamic threshold of artifact detection, BLV is the low-frequency band energy baseline value, FR is the low-frequency band energy fluctuation range, and CCF is the coupling degree confidence factor; α is the influence factor of the low-frequency energy fluctuation range, indicating the positive influence of the low-frequency energy fluctuation range on the artifact detection dynamic threshold; β is the nonlinear influence degree of the coupling confidence factor, indicating the nonlinear negative influence degree of the coupling confidence factor on the dynamic threshold of artifact detection.

[0010] The present application provides an ultrasonic endoscopic processing method, which specifies the calculation method of the dynamic threshold of artifact detection through the formula DT = BLV * (1 + α * FR / BLV) * exp(-β * (1 - CCF)). This dynamic adjustment strategy enables the dynamic threshold of artifact detection to be adaptively adjusted according to changes in coupling degree, thereby improving the accuracy and sensitivity of artifact detection.

[0011] Further, step S4 includes: S41: normalizing the low-frequency energy change rate to obtain a normalized low-frequency energy change rate; S42: According to the normalized low-frequency energy change rate, the low-frequency gain suppression factor is calculated by a nonlinear mapping function; wherein the nonlinear mapping function is a Sigmoid function, and the calculation formula is: Lf_inhibition = 1 / (1 + exp(-k * Normalized_rate)), where Lf_inhibition is the low-frequency gain inhibition factor, k is a preset Sigmoid function steepness adjustment parameter; Normalized_rate is the normalized low-frequency energy change rate.

[0012] The present application provides an ultrasonic endoscopy processing method, which can obtain a more reasonable low-frequency gain suppression factor through normalization processing and Sigmoid nonlinear mapping, so that it can be better used in subsequent gain adjustment steps to improve image quality.

[0013] Further, step S5 includes: S51: determining whether the low-frequency energy value exceeds the artifact detection dynamic threshold, and if so, determining that a bleeding artifact exists; S52: if the low-frequency energy value does not exceed the artifact detection dynamic threshold, obtaining a preset low-frequency reference gain, and using the low-frequency reference gain as the real-time low-frequency gain coefficient; 53: If the low-frequency energy value exceeds the artifact detection dynamic threshold, calculate a real-time low-frequency gain coefficient according to the artifact detection dynamic threshold, the low-frequency gain suppression factor and the low-frequency energy value.

[0014] Further, in step S53, according to the artifact detection dynamic threshold, the low-frequency gain suppression factor and the low-frequency energy value, the formula for calculating the real-time low-frequency gain coefficient is: Gain_lf = Base_gain - Lf_inhibition * (Current_lf - DT), wherein Gain_lf is the real-time low-frequency gain coefficient, Base_gain is the preset low-frequency reference gain, Lf_inhibition is the low-frequency gain inhibition factor, Current_lf is the low-frequency energy value, and DT is the artifact detection dynamic threshold.

[0015] Further, step S6 includes: S61: extracting high-frequency band energy information according to the ultrasonic echo signal, wherein the high-frequency band energy information at least includes: a high-frequency band energy value, a high-frequency band energy fluctuation range, and a high-frequency band energy change rate; S62: Obtaining a preset high frequency band energy baseline value; S63: Calculating a dynamic threshold for attenuation detection according to the high frequency band energy baseline value and the high frequency band energy fluctuation range; S64: Calculating a high-frequency gain amplification factor according to the high-frequency energy change rate; S65: Calculating a real-time high-frequency gain coefficient according to the attenuation detection dynamic threshold, the high-frequency gain amplification factor, and the high-frequency band energy value; S66: Apply the real-time low-frequency gain coefficient and the real-time high-frequency gain coefficient to the ultrasound echo signal in the frequency domain to perform gain adjustment, convert the gain-adjusted frequency domain signal into a time domain signal, perform image reconstruction, and generate an ultrasound endoscopic image.

[0016] Further, step S66 includes: S661: multiplying the real-time low-frequency gain coefficient by the low-frequency component of the ultrasonic echo signal to adjust the gain of the low-frequency component; multiplying the real-time high-frequency gain coefficient by the high-frequency component of the ultrasonic echo signal to adjust the gain of the high-frequency component; S662: Performing Gaussian filtering on the high-frequency component and the low-frequency component to smooth noise and obtain a gain-adjusted frequency domain signal; S663: Performing inverse Fourier transform on the gain-adjusted frequency domain signal to convert the frequency domain signal into a time domain signal; S664: performing envelope detection on the time domain signal, and performing logarithmic compression on the time domain signal after envelope detection to enhance image contrast; S665: Scan the time domain signal after logarithmic compression, convert the polar coordinate form of the ultrasonic echo signal into a rectangular coordinate form, perform image interpolation, and generate an ultrasonic endoscopic image.

[0017] Further, step S662 includes: S6621: Decompose the high frequency component and the low frequency component into a real signal and an imaginary signal respectively; S6622: performing Gaussian filtering on the real signal and the imaginary signal respectively to obtain a filtered real signal and a filtered imaginary signal, wherein a standard deviation of the Gaussian filter is adaptively adjusted according to a preset high-frequency noise level, and a size of the Gaussian filter matches a resolution of the ultrasound endoscopic image; S6623: synthesize the filtered real signal and the filtered imaginary signal into the filtered high-frequency component and the filtered low-frequency component, and combine the filtered high-frequency component and the filtered low-frequency component as a gain-adjusted frequency domain signal.

[0018] In a second aspect, an ultrasonic endoscopic image processing device is used in the steps of any one of the above-mentioned ultrasonic endoscopic image processing methods, and the device includes: The first acquisition module is used to acquire the ultrasonic echo signal, perform frequency domain analysis, and extract low-frequency energy information, wherein the low-frequency energy information at least includes: low-frequency energy value, low-frequency energy fluctuation range, and low-frequency energy change rate; The second acquisition module is used to obtain a preset low-frequency band energy baseline value; Dynamic threshold adjustment module: used for calculating the dynamic threshold of artifact detection according to the low-frequency band energy baseline value and the low-frequency band energy fluctuation range; Dynamic gain adjustment module: used for calculating the low-frequency gain suppression factor according to the low-frequency band energy change rate; A gain coefficient adjustment module: used for calculating a real-time low-frequency gain coefficient according to the artifact detection dynamic threshold, the low-frequency gain suppression factor and the low-frequency energy value; Image reconstruction module: applies the real-time low-frequency gain coefficient to the ultrasound echo signal in the frequency domain, performs gain adjustment, converts the gain-adjusted frequency domain signal into a time domain signal, performs image reconstruction, and generates an ultrasound endoscopic image.

[0019] Beneficial effects: The ultrasonic endoscopic image processing method and device proposed in the present application obtains ultrasonic echo signals and performs frequency domain analysis to extract low-frequency energy information, including energy value, energy fluctuation range and energy change rate, which are used for subsequent artifact detection and gain adjustment. The scheme pre-sets the low-frequency energy baseline value as an energy reference under normal conditions. Then, the dynamic threshold for artifact detection is calculated based on the low-frequency energy baseline value and the energy fluctuation range. This dynamic threshold for artifact detection can adapt to signal fluctuations and detect artifacts more accurately. The scheme also calculates the low-frequency gain suppression factor based on the low-frequency energy change rate. The low-frequency energy change rate reflects the speed of the artifact appearance and is used to dynamically adjust the gain suppression strength. The scheme combines the dynamic threshold for artifact detection, the low-frequency gain suppression factor and the current low-frequency energy value to calculate the real-time low-frequency gain coefficient and realize dynamic adjustment of the gain. Finally, the scheme applies the real-time low-frequency gain coefficient to the ultrasonic echo signal in the frequency domain, performs gain adjustment, and converts the adjusted frequency domain signal into a time domain signal to reconstruct and generate an ultrasonic endoscopic image, ultimately achieving the effect of improving the quality of ultrasonic endoscopic images. Therefore, this scheme realizes dynamic gain adjustment of ultrasound images by analyzing and utilizing the low-frequency energy information of ultrasound echo signals, thereby effectively improving the quality of ultrasound images in minimally invasive ultrasound endoscopic surgery. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of an ultrasonic endoscopic image processing method proposed in this application.

[0021] Figure 2 This is a structural diagram of an ultrasonic endoscopic image processing device proposed in this application.

[0022] Description of reference numerals: 201, image acquisition module; 202, point cloud construction module; 203, blood vessel segmentation module; 204, parameter calculation module; 205, blood vessel classification module. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application usually described and marked in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0024] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first, second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0025] In minimally invasive endoscopic ultrasound surgery under complex physiological conditions, the real-time degradation of ultrasound image quality caused by the superposition of bleeding artifacts and tissue attenuation is a key issue restricting diagnostic accuracy and surgical efficiency. Existing image processing technology is difficult to effectively cope with the dynamic changes of the physiological environment and cannot provide continuous high-quality image support. Therefore, this application proposes an endoscopic ultrasound image processing method and device, as follows: Please refer to Figure 1 In a first aspect, an endoscopic ultrasound image processing method is provided for improving the quality of ultrasound images in minimally invasive endoscopic ultrasound surgery. The method comprises the following steps: S1: Acquire ultrasonic echo signals, perform frequency domain analysis, and extract low-frequency energy information, where the low-frequency energy information at least includes: low-frequency energy value, low-frequency energy fluctuation range, and low-frequency energy change rate; S2: Obtaining a preset low-frequency band energy baseline value; S3: Calculate the dynamic threshold for artifact detection based on the low-frequency energy baseline value and the low-frequency energy fluctuation range; S4: Calculate the low-frequency gain suppression factor according to the energy change rate of the low-frequency band; S5: Calculate the real-time low-frequency gain coefficient according to the artifact detection dynamic threshold, the low-frequency gain suppression factor and the low-frequency energy value; S6: Apply the real-time low-frequency gain coefficient to the ultrasound echo signal in the frequency domain to perform gain adjustment, convert the gain-adjusted frequency domain signal into a time domain signal, perform image reconstruction, and generate an ultrasound endoscopic image.

[0026] Among them, in step S1, after the ultrasonic echo signal is acquired, frequency domain analysis will be performed, for example, by fast Fourier transform. Based on the frequency domain analysis, low-frequency band energy information is extracted, and the low-frequency band energy information at least includes the low-frequency band energy value, the low-frequency band energy fluctuation range, and the low-frequency band energy change rate. The low-frequency band energy value can reflect the strength of the current low-frequency signal. The low-frequency band energy fluctuation range can reflect the change amplitude of the low-frequency signal over a period of time. The low-frequency band energy change rate can reflect the speed of the change of the low-frequency signal.

[0027] In step S2, a preset low-frequency band energy baseline value is obtained. The low-frequency band energy baseline value is a reference value of low-frequency band energy in a normal tissue or artifact-free situation, and can be obtained in advance through experiments or experience.

[0028] In step S3, the calculation of the dynamic threshold for artifact detection takes into account the volatility of the low-frequency energy signal, so that the dynamic threshold for artifact detection can adapt to the changes in the low-frequency energy signal, thereby more accurately detecting artifacts. For example, the dynamic threshold for artifact detection can be calculated by the formula DT = BLV * (1 + α * FR / BLV), where DT is the dynamic threshold for artifact detection, BLV is the low-frequency energy baseline value, FR is the low-frequency energy fluctuation range, and α is the influencing factor.

[0029] In step S4, the low-frequency gain suppression factor is used to dynamically adjust the gain suppression strength. The greater the energy change rate, the higher the possibility of artifacts, and the low-frequency gain suppression factor should be increased accordingly. For example, the low-frequency gain suppression factor can be calculated by the Sigmoid function Lf_inhibition = 1 / (1 + exp(-k * Normalized_rate)), where Lf_inhibition is the low-frequency gain suppression factor, k is the steepness adjustment parameter, and Normalized_rate is the normalized low-frequency energy change rate.

[0030] In step S5, if the current low-frequency energy value exceeds the dynamic threshold value of artifact detection, it is determined that there may be bleeding artifacts. At this time, it is necessary to dynamically adjust the gain coefficient according to the low-frequency gain suppression factor and the low-frequency energy value to suppress the artifacts. If the low-frequency energy value does not exceed the threshold value, it is determined that there is no bleeding artifact, and the preset low-frequency reference gain can be used as the real-time low-frequency gain coefficient. For example, the real-time low-frequency gain coefficient can be calculated by the formula Gain_lf = Base_gain - Lf_inhibition * (Current_lf - DT), where Gain_lf is the real-time low-frequency gain coefficient, Base_gain is the low-frequency reference gain, Lf_inhibition is the low-frequency gain suppression factor, Current_lf is the low-frequency energy value, and DT is the dynamic threshold value of artifact detection.

[0031] In step S6, the real-time low-frequency gain coefficient is applied to the ultrasound echo signal in the frequency domain to adjust the gain. The gain-adjusted frequency domain signal is converted into a time domain signal, for example, by inverse Fourier transform. Finally, the time domain signal is used for image reconstruction to generate an ultrasound endoscopic image. The image reconstruction process may include steps such as envelope detection, logarithmic compression, and scan conversion.

[0032] Specifically, the purpose of this scheme is to improve the quality of ultrasonic images in minimally invasive endoscopic ultrasonic surgery. In view of the problem that the image quality of the existing technology is easy to deteriorate under complex physiological environments, a dynamic gain adjustment method based on low-frequency energy information is proposed. First, the ultrasonic echo signal is collected and analyzed in the frequency domain to extract the low-frequency energy information to provide data support for subsequent artifact detection and gain adjustment. The preset low-frequency energy baseline value is used as the energy reference under normal circumstances for the calculation of the dynamic threshold. The dynamic threshold for artifact detection is calculated based on the low-frequency energy baseline value and the energy fluctuation range, which realizes the adaptive adjustment of the artifact detection threshold and improves the accuracy of artifact detection. The low-frequency gain suppression factor is calculated based on the low-frequency energy change rate, which realizes the dynamic adjustment of the gain suppression intensity and can adaptively adjust the gain suppression intensity according to the speed of the artifact. The real-time low-frequency gain coefficient comprehensively considers the dynamic threshold for artifact detection, the low-frequency gain suppression factor and the current low-frequency energy value, realizes the real-time and dynamic adjustment of the gain coefficient, and can dynamically reduce the gain when the artifact is detected to suppress the influence of the artifact on the image quality. Finally, the real-time low-frequency gain coefficient is applied to the ultrasound echo signal in the frequency domain to adjust the gain, and the adjusted frequency domain signal is converted into a time domain signal to reconstruct the ultrasound endoscopic image, thereby effectively improving the quality of ultrasound images in minimally invasive ultrasound endoscopic surgery. Through the above steps, this solution can dynamically adjust the gain of the ultrasound image according to the low-frequency energy information of the ultrasound echo signal, thereby effectively suppressing bleeding artifacts, improving image quality, providing doctors with clearer ultrasound images, and assisting doctors in more accurate diagnosis and treatment.

[0033] In some specific embodiments, frequency domain analysis is implemented by short-time Fourier transform, and the low frequency band is set to the 0MHz-2MHz frequency band. The low frequency band energy value is obtained by calculating the square sum of the spectrum amplitudes in the frequency band. The low frequency band energy fluctuation range is obtained by calculating the difference between the maximum and minimum values ​​of the low frequency band energy value through a sliding window, and the length of the sliding window is set to 5 frames. The low frequency band energy change rate is obtained by calculating the difference between the low frequency band energy values ​​of the current frame and the previous frame.

[0034] The low frequency band energy baseline value is preset to 500 units of energy value.

[0035] The dynamic threshold for artifact detection is calculated using the formula DT = BLV * (1 + 0.5 * FR / BLV) * exp(-2 * (1- CCF)), where the coupling confidence factor CCF can be simplified to 1.

[0036] The normalized low-frequency energy change rate is obtained by dividing the low-frequency energy change rate by the maximum possible energy change range, and the steepness adjustment parameter k can be preset to 5.

[0037] The low frequency reference gain can be preset to 0dB.

[0038] The frequency domain signal after gain adjustment is converted into the time domain signal through inverse short-time Fourier transform. The image reconstruction process adopts the commonly used B-mode ultrasound imaging reconstruction method. Through the above specific parameters and methods, this scheme is implemented and verified to be effective in improving the quality of ultrasound endoscopic images.

[0039] Further, step S3 includes: S31: Acquire a coupling parameter between the ultrasonic endoscopic probe and the lesion area, where the coupling parameter at least includes an ultrasonic echo signal intensity; S32: Calculating a coupling degree confidence factor according to the ultrasonic echo signal strength; S33: Calculate the dynamic threshold for artifact detection according to the coupling degree confidence factor, the low-frequency band energy baseline value, and the low-frequency band energy fluctuation range.

[0040] In step S31, the coupling parameter represents the signal transmission efficiency between the ultrasonic endoscopic probe and the lesion area. The coupling parameter is obtained by a sensor, and the sensor can be configured to measure the physical contact characteristics between the probe and the tissue. The ultrasonic echo signal intensity reflects the effective transfer degree of ultrasonic energy from the probe to the tissue.

[0041] In step S32, the calculation of the coupling confidence factor is a process of quantifying the degree of coupling. The ultrasonic echo signal strength is normalized or mapped to a range of 0 to 1 to generate a coupling confidence factor. The higher the signal strength, the closer the confidence factor is to 1, indicating a better coupling. The lower the signal strength, the closer the confidence factor is to 0, indicating a worse coupling.

[0042] In step S33, the calculation formula for the dynamic threshold of artifact detection is: DT = BLV * (1 + α * FR / BLV) * exp(-β * (1 - CCF)). In the formula, the coupling confidence factor CCF is introduced to adjust the dynamic threshold DT. When the coupling confidence factor is high, the exp(-β * (1 - CCF)) term is close to 1, and the dynamic threshold of artifact detection is mainly determined by the low-frequency band energy baseline value BLV and the low-frequency band energy fluctuation range FR. When the coupling confidence factor is low, the exp(-β *(1 - CCF)) term decreases, and the dynamic threshold DT decreases accordingly. As a result, the dynamic threshold of artifact detection can be adaptively adjusted according to the coupling degree.

[0043] Further, in step S33, the formula for calculating the dynamic threshold of artifact detection according to the coupling confidence factor, the low-frequency energy baseline value, and the low-frequency energy fluctuation range is: DT = BLV * (1 + α * FR / BLV) * exp(-β *(1 - CCF)); wherein DT is the dynamic threshold of artifact detection, BLV is the low-frequency energy baseline value, FR is the low-frequency energy fluctuation range, and CCF is the coupling confidence factor; α is the influencing factor of the energy fluctuation range of the low-frequency band, which indicates the positive influence of the low-frequency band energy fluctuation range on the dynamic threshold of artifact detection; β is the nonlinear influence of the coupling confidence factor, which indicates the nonlinear negative influence of the coupling confidence factor on the dynamic threshold of artifact detection.

[0044] Among them, in some specific embodiments, the low-frequency band energy baseline value BLV can be preset to 100, the low-frequency band energy fluctuation range FR can be set to 20, the influence factor α can be set to 0.5, and the nonlinear influence factor β can be set to 2. When the coupling confidence factor CCF is 0.9, substituting into the formula to calculate DT = 100 * (1 + 0.5 * 20 / 100) * exp(-2 * (1 -0.9)) ≈ 110 * exp(-0.2) ≈ 90.2. When the coupling confidence factor CCF is reduced to 0.5, DT = 100 *(1 + 0.5 * 20 / 100) * exp(-2 * (1 - 0.5)) ≈ 110 * exp(-1) ≈ 40.4. It can be seen that as the coupling confidence factor CCF decreases, the dynamic threshold DT for artifact detection also decreases significantly. This adjustment of the dynamic threshold of artifact detection can adapt to the artifact detection needs under different coupling conditions, and can still maintain a high artifact detection sensitivity when the coupling degree is poor.

[0045] Further, step S4 includes: S41: normalizing the low-frequency energy change rate to obtain a normalized low-frequency energy change rate; S42: According to the normalized low-frequency energy change rate, a low-frequency gain suppression factor is calculated by a nonlinear mapping function; wherein the nonlinear mapping function is a Sigmoid function, and the calculation formula is: Lf_inhibition = 1 / (1 + exp(-k * Normalized_rate)), where Lf_inhibition is the low-frequency gain inhibition factor, k is the preset Sigmoid function steepness adjustment parameter; Normalized_rate is the normalized low-frequency energy change rate.

[0046] In step S41, the low-frequency energy change rate is normalized, and a linear transformation method can be used to map the low-frequency energy change rate to the [0, 1] interval. For example, assuming that the original value range of the low-frequency energy change rate is [Rate_min, Rate_max], the calculation formula of the normalized low-frequency energy change rate can be: Normalized_rate = (Current_rate - Rate_min) / (Rate_max - Rate_min), where Current_rate is the current low-frequency energy change rate.

[0047] In step S42, the Sigmoid function is an S-shaped curve function with an output value range of (0, 1), which is very suitable for generating a suppression factor with a value between 0 and 1. The steepness adjustment parameter k controls the shape of the Sigmoid function. The larger the k value, the steeper the Sigmoid function, and the more sensitive the low-frequency gain suppression factor is to the change of the normalized low-frequency energy change rate. The k value can be pre-set according to the actual application scenario and empirical data. For example, the k value can be set to 5. By adjusting the k value, the response degree of the low-frequency gain suppression factor to the energy change rate can be finely adjusted, thereby optimizing the image artifact suppression effect.

[0048] This scheme combines normalization processing with Sigmoid nonlinear mapping to obtain a more reasonable low-frequency gain suppression factor, which can be better used for subsequent gain adjustment to improve image quality.

[0049] Further, step S5 includes: S51: determining whether the low-frequency energy value exceeds the dynamic threshold of artifact detection, and if so, determining that a bleeding artifact exists; S52: If the low-frequency energy value does not exceed the artifact detection dynamic threshold, a preset low-frequency reference gain is obtained, and the low-frequency reference gain is used as a real-time low-frequency gain coefficient; 53: If the low frequency energy value exceeds the artifact detection dynamic threshold, a real-time low frequency gain coefficient is calculated according to the artifact detection dynamic threshold, the low frequency gain suppression factor and the low frequency energy value.

[0050] Among them, the judgment processing is performed through step S51 to determine the size relationship between the low-frequency energy value and the dynamic threshold of artifact detection, so as to determine whether there is a bleeding artifact. Specifically, the currently acquired low-frequency energy value is compared with the pre-calculated dynamic threshold of artifact detection. When the low-frequency energy value is greater than the dynamic threshold of artifact detection, the system determines that there is a bleeding artifact in the current ultrasonic endoscopic image. This judgment provides a basis for whether to perform gain adjustment later.

[0051] In step S52, it is determined that the low-frequency energy value does not exceed the dynamic threshold value for artifact detection, which means that the current image may not be affected by obvious bleeding artifacts. As a processing method, the system can directly use the preset low-frequency reference gain as the real-time low-frequency gain coefficient. The low-frequency reference gain is a preset stable gain value used when no bleeding artifacts are detected, in order to maintain the original state of the image and avoid unnecessary gain adjustment.

[0052] In step S53, when the judgment result is that the low-frequency energy value exceeds the dynamic threshold of artifact detection, indicating the presence of bleeding artifacts, a real-time low-frequency gain coefficient is calculated based on the dynamic threshold of artifact detection, the low-frequency gain suppression factor and the low-frequency energy value. The calculated real-time low-frequency gain coefficient will be used for subsequent gain adjustment to achieve the effect of suppressing bleeding artifacts.

[0053] In some specific embodiments, in minimally invasive endoscopic ultrasound surgery, after the ultrasonic probe acquires the ultrasonic echo signal, the device first performs frequency domain analysis to extract the low-frequency energy value. Assume that the low-frequency energy value of the current frame ultrasound image is 80 units, and the pre-calculated dynamic threshold for artifact detection is 70 units. In step S51, the system compares 80 and 70, and determines that the low-frequency energy value exceeds the dynamic threshold for artifact detection, so it is determined that there may be a bleeding artifact. At this time, step S52 is skipped and step S53 is executed directly. In step S53, it is assumed that the low-frequency gain suppression factor calculated according to step S4 is 0.5, and the preset low-frequency reference gain is 10dB. According to the formula Gain_lf = Base_gain - Lf_inhibition * (Current_lf -DT), the real-time low-frequency gain coefficient Gain_lf = 10 - 0.5 * (80 - 70) = 5dB is calculated. Finally, the system will use 5dB as the real-time low-frequency gain coefficient to adjust the gain of the low-frequency component of the ultrasonic echo signal to suppress bleeding artifacts and improve image quality. On the contrary, if the low-frequency energy value is 60 units and does not exceed the threshold of 70 units, step S52 is executed to directly use the low-frequency reference gain of 10dB without performing artifact suppression-related gain adjustment. In this way, the technical effect of maintaining the original gain of the image when there is no bleeding artifact and performing gain adjustment only when bleeding artifacts are detected is achieved.

[0054] By determining whether bleeding artifacts are present in the present application, gain adjustment can be performed on demand. Gain adjustment is performed only when bleeding artifacts are detected, and the original gain is maintained when there are no artifacts. This selective gain adjustment strategy can retain normal image information as much as possible while ensuring the artifact suppression effect, thereby improving the efficiency and pertinence of image processing.

[0055] Further, in step S53, according to the artifact detection dynamic threshold, the low-frequency gain suppression factor and the low-frequency energy value, the formula for calculating the real-time low-frequency gain coefficient is: Gain_lf = Base_gain - Lf_inhibition * (Current_lf - DT), where Gain_lf is the real-time low-frequency gain coefficient, Base_gain is the preset low-frequency reference gain, Lf_inhibition is the low-frequency gain inhibition factor, Current_lf is the low-frequency energy value, and DT is the dynamic threshold for artifact detection.

[0056] Among them, the calculation formula of the real-time low-frequency gain coefficient reflects the calculation method of the real-time low-frequency gain coefficient. Specifically, the calculation method uses the preset low-frequency reference gain as the initial value, and dynamically adjusts according to the low-frequency gain suppression factor and the difference between the current low-frequency energy value and the dynamic threshold of artifact detection. When the low-frequency energy value exceeds the dynamic threshold of artifact detection, the difference is positive, and the real-time low-frequency gain coefficient will be reduced on the basis of the low-frequency reference gain, thereby achieving the suppression of bleeding artifacts. The low-frequency gain suppression factor is used to adjust the degree of suppression, achieving fine control of gain suppression.

[0057] Through the above formula, the real-time low-frequency gain coefficient Gain_lf can be adaptively adjusted according to the degree of artifacts, achieving refined gain control and improving the quality of ultrasound endoscopy images under complex physiological environments.

[0058] Further, step S6 includes: S61: extracting high-frequency band energy information according to the ultrasonic echo signal, where the high-frequency band energy information at least includes: high-frequency band energy value, high-frequency band energy fluctuation range, and high-frequency band energy change rate; S62: Obtaining a preset high frequency band energy baseline value; S63: Calculating a dynamic threshold for attenuation detection according to a high-frequency band energy baseline value and a high-frequency band energy fluctuation range; S64: Calculate the high-frequency gain amplification factor according to the high-frequency energy change rate; S65: Calculating a real-time high-frequency gain coefficient according to the attenuation detection dynamic threshold, the high-frequency gain amplification factor, and the high-frequency band energy value; S66: Apply the real-time low-frequency gain coefficient and the real-time high-frequency gain coefficient to the ultrasound echo signal in the frequency domain to perform gain adjustment, convert the gain-adjusted frequency domain signal into a time domain signal, perform image reconstruction, and generate an ultrasound endoscopic image.

[0059] Among them, in step S61, after the ultrasonic echo signal is received, it is decomposed into components of different frequencies through a frequency domain analysis method, such as Fourier transform. The high-frequency band energy information extraction is to calculate the energy in a pre-set high-frequency range in the frequency domain signal. The high-frequency band energy value can be obtained by calculating the square sum or average value of the high-frequency component amplitude. The high-frequency band energy fluctuation range can be obtained by counting the difference between the maximum and minimum values ​​of the high-frequency band energy value over a period of time. The high-frequency band energy change rate can be obtained by calculating the change in the high-frequency band energy value per unit time.

[0060] In step S62, the preset high-frequency band energy baseline value represents the high-frequency band energy level under an ideal tissue state, that is, when there is no obvious attenuation. This baseline value can be obtained through experimental calibration, empirical value setting, or automatically when the system is initialized.

[0061] In step S63, the dynamic threshold of attenuation detection can be a threshold of adaptive tissue attenuation. The calculation formula of the dynamic threshold of attenuation detection uses the high-frequency band energy baseline value and the high-frequency band energy fluctuation range, with the purpose of setting a reasonable attenuation detection standard according to the characteristics of the tissue itself and the signal fluctuation. For example, the formula can be designed as a dynamic threshold equal to the high-frequency band energy baseline value plus a term related to the high-frequency band energy fluctuation range, so as to adapt to the energy fluctuation characteristics of different tissues.

[0062] In step S64, the high-frequency gain amplification factor is a coefficient used to compensate for the attenuation of the high-frequency signal. The high-frequency energy change rate reflects the speed or degree of the attenuation of the high-frequency signal, so the high-frequency gain amplification factor can be dynamically adjusted according to the high-frequency energy change rate. For example, when the high-frequency energy change rate indicates that the signal is rapidly attenuated, the high-frequency gain amplification factor is increased, and vice versa. The calculation of the high-frequency gain amplification factor can be achieved by a linear function, a nonlinear function, or a lookup table mapping.

[0063] In step S65, the real-time high-frequency gain coefficient is the gain value finally applied to the high-frequency signal. The calculation of the real-time high-frequency gain coefficient comprehensively considers the attenuation detection dynamic threshold, the high-frequency gain amplification factor and the current high-frequency band energy value. If the current high-frequency band energy value is lower than the attenuation detection dynamic threshold, it indicates that tissue attenuation may exist. At this time, it is necessary to calculate the real-time high-frequency gain coefficient based on the high-frequency gain amplification factor and the difference between the current energy value and the threshold to compensate for the attenuation. The calculation formula can be designed as a real-time high-frequency gain coefficient equal to a basic high-frequency gain plus a term related to the high-frequency gain amplification factor and the energy value difference.

[0064] In step S66, the real-time low-frequency gain coefficient and the real-time high-frequency gain coefficient are applied to the low-frequency component and the high-frequency component of the frequency-domain ultrasonic echo signal, respectively. The gain-adjusted frequency-domain signal is converted back to the time-domain signal by methods such as inverse Fourier transform. The time-domain signal undergoes subsequent image reconstruction processing, such as envelope detection, logarithmic compression, scan conversion, and image interpolation, and finally generates an ultrasonic endoscopic image. By adjusting the low-frequency and high-frequency gains at the same time, it is possible to suppress artifacts while compensating for tissue attenuation and improving the overall quality of the image. In this way, clearer and more accurate ultrasonic endoscopic images can be obtained to assist doctors in diagnosis and surgical operations.

[0065] Further, step S66 includes: S661: multiplying the real-time low-frequency gain coefficient by the low-frequency component of the ultrasonic echo signal to adjust the gain of the low-frequency component; multiplying the real-time high-frequency gain coefficient by the high-frequency component of the ultrasonic echo signal to adjust the gain of the high-frequency component; S662: Performing Gaussian filtering on the high-frequency component and the low-frequency component to smooth the noise and obtain a frequency domain signal after gain adjustment; S663: Perform inverse Fourier transform on the gain-adjusted frequency domain signal to convert the frequency domain signal into a time domain signal; S664: performing envelope detection on the time domain signal, and performing logarithmic compression on the time domain signal after envelope detection to enhance image contrast; S665: Scan the time domain signal after logarithmic compression, convert the polar coordinate form of the ultrasound echo signal into a rectangular coordinate form, perform image interpolation, and generate an ultrasound endoscopic image.

[0066] Among them, in step S661, the real-time low-frequency gain coefficient and the real-time high-frequency gain coefficient are applied to the low-frequency component and the high-frequency component of the ultrasonic echo signal, respectively. Specifically, in the frequency domain, the ultrasonic echo signal is decomposed into two components, low-frequency and high-frequency. The calculated real-time low-frequency gain coefficient is multiplied with the low-frequency component to adjust the low-frequency signal strength. Similarly, the calculated real-time high-frequency gain coefficient is multiplied with the high-frequency component to adjust the high-frequency signal strength. In this way, independent gain control can be performed for different frequency components to more finely compensate for signal attenuation and suppress artifacts.

[0067] In step S662, Gaussian filtering is used to smooth noise. Specifically, the frequency domain signal after gain adjustment, including low-frequency components and high-frequency components, will be Gaussian filtered separately. Gaussian filter is a linear smoothing filter that can effectively reduce random noise in the signal. The standard deviation of the Gaussian filter can be adaptively adjusted according to the preset high-frequency noise level. The size of the filter is matched with the resolution of the ultrasonic endoscopic image to ensure the filtering effect while avoiding over-smoothing of image details. Through Gaussian filtering, the signal-to-noise ratio of the image can be improved, making the image clearer.

[0068] In step S663, the inverse Fourier transform is used to convert the frequency domain signal back to the time domain signal. Specifically, the frequency domain signal after gain adjustment and filtering needs to be converted back to the time domain through the inverse Fourier transform for subsequent image reconstruction operations. The inverse Fourier transform is the inverse process of the Fourier transform, which can convert the signal from the frequency domain representation back to the time domain representation. Thus, the signal after frequency domain processing can be restored to the ultrasonic echo signal in the time domain.

[0069] In step S664, envelope detection and logarithmic compression are used to enhance the image contrast. Specifically, the time domain signal is first envelope detected to extract the amplitude information of the ultrasonic echo signal to obtain the envelope of the signal. Then, the signal after envelope detection is logarithmically compressed. Logarithmic compression can expand the dynamic range of the image and map the amplitude value to a grayscale range that is more suitable for human eye observation. In this way, the contrast of the image can be enhanced, making the tissue structure and lesion details in the image more clearly visible.

[0070] In step S665, scan conversion and image interpolation are used to generate an endoscopic ultrasound image. Specifically, the time domain signal after logarithmic compression, which is usually ultrasound echo data expressed in polar coordinates, needs to be converted into rectangular coordinates through scan conversion so that the image can be presented on a display device. The scan conversion process is usually accompanied by image interpolation operations, such as linear interpolation or bilinear interpolation, which are used to fill pixel gaps and generate a continuous and smooth endoscopic ultrasound image. Thus, an endoscopic ultrasound image suitable for doctors to observe and diagnose is finally generated.

[0071] Further, step S662 includes: S6621: Decompose the high frequency component and the low frequency component into a real signal and an imaginary signal respectively; S6622: performing Gaussian filtering on the real signal and the imaginary signal respectively to obtain a filtered real signal and a filtered imaginary signal, wherein a standard deviation of the Gaussian filter is adaptively adjusted according to a preset high-frequency noise level, and a size of the Gaussian filter matches a resolution of the ultrasound endoscopic image; S6623: synthesize the filtered real signal and the filtered imaginary signal into filtered high-frequency components and low-frequency components, and combine the filtered high-frequency components and low-frequency components as a gain-adjusted frequency domain signal.

[0072] In step S6621, after the ultrasonic echo signal is decomposed into high-frequency components and low-frequency components in the frequency domain, each frequency component exists in a complex form, including a real part and an imaginary part. The decomposition into real and imaginary signals is to enable more refined processing of the two components of the signal in the subsequent process.

[0073] In step S6622, the decomposed real signal and imaginary signal are Gaussian filtered respectively. The standard deviation of the Gaussian filter is adaptively adjusted based on the preset high-frequency noise level. When the high-frequency noise level is high, a Gaussian filter with a larger standard deviation is used, and the filtering strength is increased accordingly, thereby achieving more effective noise smoothing. Conversely, when the noise level is low, a Gaussian filter with a smaller standard deviation is used to avoid over-smoothing image details, thereby ensuring the resolution of the image. In addition, the size of the Gaussian filter matches the resolution of the ultrasound endoscopic image, ensuring that the filtering operation effectively reduces noise while maintaining image clarity. For example, the Gaussian filter size can be set to a size corresponding to one or several pixels in the image.

[0074] In step S6623, the filtered real signal and the imaginary signal are recombined to form filtered high-frequency components and low-frequency components. Finally, the filtered high-frequency components and low-frequency components are combined to form a gain-adjusted frequency domain signal, providing higher quality frequency domain data for subsequent image reconstruction steps. Through the above-mentioned refined Gaussian filtering processing, the noise in the ultrasound endoscopic image can be more effectively removed, and the signal-to-noise ratio and visual quality of the image are improved to assist doctors in making more accurate diagnoses.

[0075] In a second aspect, an ultrasonic endoscopic image processing device is used in any of the steps of an ultrasonic endoscopic image processing method described above, and the device includes: The first acquisition module 201 is used to acquire the ultrasonic echo signal, perform frequency domain analysis, and extract low-frequency energy information, where the low-frequency energy information at least includes: low-frequency energy value, low-frequency energy fluctuation range, and low-frequency energy change rate; The second acquisition module 202 is used to acquire a preset low frequency band energy baseline value; Dynamic threshold adjustment module 203: used to calculate the dynamic threshold for artifact detection according to the low-frequency band energy baseline value and the low-frequency band energy fluctuation range; Dynamic gain adjustment module 204: used to calculate the low-frequency gain suppression factor according to the low-frequency energy change rate; The gain coefficient adjustment module 205 is used to calculate the real-time low-frequency gain coefficient according to the artifact detection dynamic threshold, the low-frequency gain suppression factor and the low-frequency energy value; Image reconstruction module 206: applies the real-time low-frequency gain coefficient to the ultrasound echo signal in the frequency domain, performs gain adjustment, converts the gain-adjusted frequency domain signal into a time domain signal, performs image reconstruction, and generates an ultrasound endoscopic image.

[0076] Among them, the first acquisition module 201 can receive an ultrasonic echo signal from an ultrasonic endoscope probe. The received signal is then used for frequency domain analysis, which can be achieved by fast Fourier transform. After the frequency domain analysis, the low-frequency energy information of the ultrasonic echo signal is extracted. The low-frequency energy information includes the low-frequency energy value, which reflects the intensity of the low-frequency component; the low-frequency energy fluctuation range, which indicates the amplitude of the change of the low-frequency energy over time; and the low-frequency energy change rate, which indicates the speed of the change of the low-frequency energy.

[0077] The second acquisition module 202 is used to provide a preset low frequency band energy baseline value, which represents a normal low frequency energy level without artifacts or interference, and can be stored in the memory of the device or preset by the user.

[0078] The dynamic threshold adjustment module 203 uses the low frequency energy baseline value provided by the second acquisition module 202 and the low frequency energy fluctuation range extracted by the first acquisition module 201 to calculate the dynamic threshold for artifact detection. The calculation of the dynamic threshold for artifact detection takes into account the normal fluctuation of low frequency energy, making artifact detection more flexible and accurate.

[0079] The dynamic gain adjustment module 204 calculates the low-frequency gain suppression factor based on the low-frequency energy change rate provided by the first acquisition module 201. The low-frequency gain suppression factor is used to control the adjustment degree of the low-frequency gain. The higher the change rate, the greater the suppression factor may be, thereby achieving suppression of artifacts.

[0080] The gain coefficient adjustment module 205 is the core component of the device, which calculates the real-time low-frequency gain coefficient by comprehensively considering the dynamic threshold of artifact detection calculated by the dynamic threshold adjustment module 203, the low-frequency gain suppression factor calculated by the dynamic gain adjustment module 204, and the current low-frequency energy value extracted by the first acquisition module 201. The real-time low-frequency gain coefficient will be used in the subsequent gain adjustment steps.

[0081] The image reconstruction module 206 applies the real-time low-frequency gain coefficient calculated by the gain coefficient adjustment module to the ultrasound echo signal in the frequency domain to achieve gain adjustment of the ultrasound echo signal. The gain-adjusted frequency domain signal is then converted back to a time domain signal, for example, by inverse Fourier transform to achieve conversion from the frequency domain to the time domain. Finally, the time domain signal is used for image reconstruction to generate an ultrasound endoscopic image for observation and diagnosis by doctors.

[0082] In some specific embodiments, the first acquisition module 201 can be implemented by a high-speed analog-to-digital converter and a digital signal processor. The analog-to-digital converter is responsible for converting the received analog ultrasonic echo signal into a digital signal, and the digital signal processor performs frequency domain analysis and energy information extraction algorithms. The frequency domain analysis algorithm can use fast Fourier transform, and low-frequency energy information extraction can be implemented by a bandpass filter and an energy calculation unit.

[0083] The second acquisition module 202 may be composed of a memory, in which a low-frequency band energy baseline value is pre-stored. The low-frequency band energy baseline value may be pre-set according to experimental data or clinical experience.

[0084] The dynamic threshold adjustment module 203, the dynamic gain adjustment module 204 and the gain coefficient adjustment module 205 can be implemented by programmable logic devices or microcontrollers, which run pre-programmed algorithms to calculate the artifact detection dynamic threshold, low-frequency gain suppression factor and real-time low-frequency gain coefficient in real time based on the input low-frequency energy information, low-frequency energy baseline value and low-frequency energy fluctuation range.

[0085] The image reconstruction module 206 may be composed of a graphics processor and image processing software. The graphics processor is responsible for high-speed signal processing and image calculation, and the image processing software executes the signal-to-image conversion and reconstruction algorithm, including inverse Fourier transform, envelope detection, logarithmic compression, and scan conversion.

[0086] For example, when the ultrasonic endoscopic probe scans an area rich in blood vessels, the first acquisition module 201 detects that the low-frequency energy value and energy change rate increase, and the dynamic threshold adjustment module 203 and the dynamic gain adjustment module 204 adjust the artifact detection dynamic threshold and the low-frequency gain suppression factor accordingly. The gain coefficient adjustment module 205 calculates the reduced real-time low-frequency gain coefficient based on these parameters, and the image reconstruction module 206 uses this gain coefficient to process the ultrasonic signal. In the final generated ultrasonic endoscopic image, the bleeding artifact is effectively suppressed and the image quality is improved.

[0087] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0088] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. An endoscopic ultrasound image processing method for improving the quality of ultrasound images in minimally invasive endoscopic ultrasound surgery, characterized in that: The method comprises the steps of: S1: Acquire an ultrasonic echo signal, perform frequency domain analysis, and extract low-frequency energy information, wherein the low-frequency energy information at least includes: a low-frequency energy value, a low-frequency energy fluctuation range, and a low-frequency energy change rate; S2: Obtaining a preset low-frequency band energy baseline value; S3: calculating a dynamic threshold for artifact detection according to the low-frequency band energy baseline value and the low-frequency band energy fluctuation range; S4: Calculating a low-frequency gain suppression factor according to the low-frequency band energy change rate; S5: Calculating a real-time low-frequency gain coefficient according to the artifact detection dynamic threshold, the low-frequency gain suppression factor and the low-frequency energy value; S6: Apply the real-time low-frequency gain coefficient to the ultrasound echo signal in the frequency domain to perform gain adjustment, convert the gain-adjusted frequency domain signal into a time domain signal, perform image reconstruction, and generate an ultrasound endoscopic image.

2. The method for processing endoscopic ultrasound images according to claim 1, characterized in that: Step S3 includes: S31: Acquire a coupling parameter between the ultrasonic endoscopic probe and the lesion area, wherein the coupling parameter at least includes an ultrasonic echo signal intensity; S32: Calculating a coupling degree confidence factor according to the ultrasonic echo signal strength; S33: Calculate the artifact detection dynamic threshold according to the coupling degree confidence factor, the low-frequency band energy baseline value, and the low-frequency band energy fluctuation range.

3. The method for processing endoscopic ultrasound images according to claim 2, characterized in that: In step S33, the formula for calculating the dynamic threshold of artifact detection according to the coupling confidence factor, the low-frequency energy baseline value, and the low-frequency energy fluctuation range is: DT = BLV * (1 + α * FR / BLV) * exp(-β * (1 - CCF)); wherein DT is the dynamic threshold of artifact detection, BLV is the low-frequency energy baseline value, FR is the low-frequency energy fluctuation range, and CCF is the coupling confidence factor; α is the influence factor of the low-frequency energy fluctuation range, indicating the positive influence of the low-frequency energy fluctuation range on the artifact detection dynamic threshold; β is the nonlinear influence degree of the coupling confidence factor, indicating the nonlinear negative influence degree of the coupling confidence factor on the dynamic threshold of artifact detection.

4. The method for processing endoscopic ultrasound images according to claim 1, characterized in that: Step S4 includes: S41: normalizing the low-frequency energy change rate to obtain a normalized low-frequency energy change rate; S42: According to the normalized low-frequency energy change rate, the low-frequency gain suppression factor is calculated by a nonlinear mapping function; wherein the nonlinear mapping function is a Sigmoid function, and the calculation formula is: Lf_inhibition = 1 / (1 + exp(-k * Normalized_rate)), where Lf_inhibition is the low-frequency gain inhibition factor, k is a preset Sigmoid function steepness adjustment parameter; Normalized_rate is the normalized low-frequency energy change rate.

5. The method for processing endoscopic ultrasound images according to claim 1, characterized in that: Step S5 includes: S51: determining whether the low-frequency energy value exceeds the artifact detection dynamic threshold, and if so, determining that a bleeding artifact exists; S52: if the low-frequency energy value does not exceed the artifact detection dynamic threshold, obtaining a preset low-frequency reference gain, and using the low-frequency reference gain as the real-time low-frequency gain coefficient; 53: If the low-frequency energy value exceeds the artifact detection dynamic threshold, calculate a real-time low-frequency gain coefficient according to the artifact detection dynamic threshold, the low-frequency gain suppression factor and the low-frequency energy value.

6. The method for processing endoscopic ultrasound images according to claim 5, characterized in that: In step S53, according to the artifact detection dynamic threshold, the low-frequency gain suppression factor and the low-frequency energy value, the formula for calculating the real-time low-frequency gain coefficient is: Gain_lf = Base_gain - Lf_inhibition * (Current_lf - DT), wherein Gain_lf is the real-time low-frequency gain coefficient, Base_gain is the preset low-frequency reference gain, Lf_inhibition is the low-frequency gain inhibition factor, Current_lf is the low-frequency energy value, and DT is the artifact detection dynamic threshold.

7. The method for processing endoscopic ultrasound images according to claim 1, characterized in that: Step S6 includes: S61: extracting high-frequency band energy information according to the ultrasonic echo signal, wherein the high-frequency band energy information at least includes: a high-frequency band energy value, a high-frequency band energy fluctuation range, and a high-frequency band energy change rate; S62: Obtaining a preset high frequency band energy baseline value; S63: Calculating a dynamic threshold for attenuation detection according to the high frequency band energy baseline value and the high frequency band energy fluctuation range; S64: Calculating a high-frequency gain amplification factor according to the high-frequency energy change rate; S65: Calculating a real-time high-frequency gain coefficient according to the attenuation detection dynamic threshold, the high-frequency gain amplification factor, and the high-frequency band energy value; S66: Apply the real-time low-frequency gain coefficient and the real-time high-frequency gain coefficient to the ultrasound echo signal in the frequency domain to perform gain adjustment, convert the gain-adjusted frequency domain signal into a time domain signal, perform image reconstruction, and generate an ultrasound endoscopic image.

8. The method for processing endoscopic ultrasound images according to claim 7, characterized in that: Step S66 includes: S661: multiplying the real-time low-frequency gain coefficient by the low-frequency component of the ultrasonic echo signal to adjust the gain of the low-frequency component; multiplying the real-time high-frequency gain coefficient by the high-frequency component of the ultrasonic echo signal to adjust the gain of the high-frequency component; S662: Performing Gaussian filtering on the high-frequency component and the low-frequency component to smooth noise and obtain a gain-adjusted frequency domain signal; S663: Performing inverse Fourier transform on the gain-adjusted frequency domain signal to convert the frequency domain signal into a time domain signal; S664: performing envelope detection on the time domain signal, and performing logarithmic compression on the time domain signal after envelope detection to enhance image contrast; S665: Scan the time domain signal after logarithmic compression, convert the polar coordinate form of the ultrasonic echo signal into a rectangular coordinate form, perform image interpolation, and generate an ultrasonic endoscopic image.

9. The method for processing endoscopic ultrasound images according to claim 7, characterized in that: Step S662 includes: S6621: Decompose the high frequency component and the low frequency component into a real signal and an imaginary signal respectively; S6622: performing Gaussian filtering on the real signal and the imaginary signal respectively to obtain a filtered real signal and a filtered imaginary signal, wherein a standard deviation of the Gaussian filter is adaptively adjusted according to a preset high-frequency noise level, and a size of the Gaussian filter matches a resolution of the ultrasound endoscopic image; S6623: synthesize the filtered real signal and the filtered imaginary signal into the filtered high-frequency component and the filtered low-frequency component, and combine the filtered high-frequency component and the filtered low-frequency component as a gain-adjusted frequency domain signal.

10. An ultrasonic endoscopic image processing device, characterized in that: The device is applied in any of the steps of an endoscopic ultrasound image processing method described above, and the device comprises: The first acquisition module is used to acquire the ultrasonic echo signal, perform frequency domain analysis, and extract low-frequency energy information, wherein the low-frequency energy information at least includes: low-frequency energy value, low-frequency energy fluctuation range, and low-frequency energy change rate; The second acquisition module is used to obtain a preset low-frequency band energy baseline value; Dynamic threshold adjustment module: used for calculating the dynamic threshold of artifact detection according to the low-frequency band energy baseline value and the low-frequency band energy fluctuation range; Dynamic gain adjustment module: used for calculating the low-frequency gain suppression factor according to the low-frequency band energy change rate; A gain coefficient adjustment module: used for calculating a real-time low-frequency gain coefficient according to the artifact detection dynamic threshold, the low-frequency gain suppression factor and the low-frequency energy value; Image reconstruction module: applies the real-time low-frequency gain coefficient to the ultrasound echo signal in the frequency domain, performs gain adjustment, converts the gain-adjusted frequency domain signal into a time domain signal, performs image reconstruction, and generates an ultrasound endoscopic image.

Images