Method for evaluating acquisition quality of ultrasonic images and ultrasonic imaging device

Abstract

An embodiment of the present application discloses a method for evaluating the acquisition quality of an ultrasonic image and an ultrasonic imaging device. The method of the embodiment of the present application includes: the acquired ultrasonic image includes an ultrasonic grayscale image and a sampling image superimposed and displayed in a sampling frame of the ultrasonic grayscale image. The sampling image may include a color Doppler image, an elasticity image, an energy Doppler image or a vector blood flow image. The lesion area in the ultrasonic image can be determined, the overlap between the lesion area and the sampling frame can be determined, and the evaluation result of the acquisition quality of the ultrasonic image can be determined based on the overlap. By providing a method for evaluating the acquisition quality of an ultrasonic image, it is beneficial to reduce the possibility of doctors re-scanning and improve the diagnostic efficiency and the accuracy of the diagnostic results.

Description

[0001] This application claims priority to a Chinese patent application filed with the Patent Office of China on December 16, 2019, with application number 201911295919.7 and invention name “A method for evaluating the acquisition quality of ultrasound images and an ultrasound imaging device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of ultrasound technology, and in particular to a method for evaluating the acquisition quality of ultrasound images and an ultrasound imaging device. Background Art

[0003] Ultrasound diagnosis is a diagnostic method that applies ultrasound technology to the human body. By scanning human tissue to obtain ultrasound images, the system can analyze the tissue's data and morphology, identify diseases, and provide prompts. The operator uses the ultrasound imaging system's probe to scan the patient's examination area (such as the breast, thyroid, or uterus). When a lesion is detected, the ultrasound image is saved. The doctor or intelligent analysis software can then use the saved ultrasound image to determine the ultrasound analysis results for the lesion, such as its location, size, shape, and echogenicity.

[0004] When scanning medical personnel, operators often lack experience or make operational errors, resulting in poor quality ultrasound images of lesions. This affects the diagnostic analysis results of the lesions by doctors or intelligent analysis software, and increases the possibility of re-scanning. Summary of the Invention

[0005] The present application provides a method for evaluating the acquisition quality of an ultrasonic image and an ultrasonic imaging device, which are used to improve the efficiency of diagnosis and the accuracy of diagnosis results.

[0006] A first aspect of an embodiment of the present application provides a method for evaluating the acquisition quality of an ultrasound image, comprising: acquiring an ultrasound image of a target tissue, the ultrasound image comprising an ultrasound grayscale image, and a sampling image superimposed and displayed within a sampling frame of the ultrasound grayscale image, the sampling image comprising a color Doppler image, an elasticity image, an energy Doppler image, or a vector blood flow image; determining a lesion area in the ultrasound image; determining a degree of overlap between the lesion area and the sampling frame; and determining an evaluation result of the acquisition quality of the ultrasound image based on the degree of overlap.

[0007] A second aspect of the embodiments of the present application provides an ultrasonic imaging device, comprising:

[0008] Probe;

[0009] a transmitting circuit, wherein the transmitting circuit excites the probe to transmit ultrasonic waves toward the target tissue;

[0010] a receiving circuit, wherein the receiving circuit controls the probe to receive the ultrasonic echo returned from the target tissue to obtain an ultrasonic echo signal;

[0011] a processor, wherein the processor processes the ultrasonic echo signal to obtain an ultrasonic image of the target tissue;

[0012] a display, wherein the display displays the ultrasound image;

[0013] The processor is used to perform the following steps: acquiring an ultrasonic image of the target tissue, the ultrasonic image including an ultrasonic grayscale image, and a sampling image superimposed and displayed within a sampling frame of the ultrasonic grayscale image, the sampling image including a color Doppler image, an elasticity image, a power Doppler image, or a vector blood flow image; determining a lesion area in the ultrasonic image; determining a degree of overlap between the lesion area and the sampling frame; and determining an evaluation result of the acquisition quality of the ultrasonic image based on the degree of overlap.

[0014] A third aspect of an embodiment of the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, it enables the computer to execute the ultrasound image acquisition quality assessment method provided by the first aspect.

[0015] In the method provided in the first aspect of the embodiment of the present application, the acquired ultrasound image includes an ultrasound grayscale image and a color Doppler image or elasticity image superimposed and displayed in a sampling frame of the ultrasound grayscale image. The lesion area in the ultrasound image can be determined, the overlap between the lesion area and the sampling frame can be determined, and the evaluation result of the acquisition quality of the ultrasound image can be determined based on the overlap. By providing a method for evaluating the acquisition quality of ultrasound images, it is beneficial to reduce the possibility of doctors re-scanning and improve diagnostic efficiency and the accuracy of diagnostic results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic structural block diagram of an ultrasonic imaging device according to an embodiment of the present application;

[0017] Figure 2 This is a schematic diagram of an embodiment of a method for evaluating the acquisition quality of ultrasound images of the present application;

[0018] Figure 3 yes Figure 2 A schematic diagram of a specific implementation of step 201 in the corresponding embodiment;

[0019] Figure 4 is a schematic diagram of another embodiment of the method for evaluating the acquisition quality of ultrasound images of the present application;

[0020] Figure 5 is a schematic diagram of another embodiment of the method for evaluating the acquisition quality of ultrasound images of the present application;

[0021] Figure 6 It is a schematic diagram of an embodiment of an ultrasonic image processing device of the present application. DETAILED DESCRIPTION

[0022] The embodiments of the present application provide a method and apparatus for evaluating the acquisition quality of an ultrasound image, which are used to assist an operator in performing quality evaluation on an acquired ultrasound image.

[0023] The terms "first," "second," "third," "fourth," and the like (if any) in the specification and claims of this application and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequential sequence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] Figure 1 1 is a schematic block diagram of the structure of the ultrasonic imaging device 10 in an embodiment of the present application. The ultrasonic imaging device 10 may include a probe 100, a transmitting circuit 101, a transmit / receive selection switch 102, a receiving circuit 103, a beamforming circuit 104, a processor 105, a display 106, and a memory 107. The transmitting circuit 101 can excite the probe 100 to transmit ultrasonic waves to the target area. The receiving circuit 103 can receive the ultrasonic echo returned from the target area through the probe 100, thereby obtaining an ultrasonic echo signal / data. The ultrasonic echo signal / data is sent to the processor 105 after being subjected to beamforming processing by the beamforming circuit 104. The processor 105 processes the ultrasonic echo signal / data to obtain an ultrasonic image of the target object or an ultrasonic image of an interventional object. The ultrasonic images obtained by the processor 105 can be stored in the memory 107. These ultrasonic images can be displayed on the display 106.

[0025] In one embodiment of the present application, the display 106 of the aforementioned ultrasonic imaging device 10 may be a touch screen, a liquid crystal display, etc., or it may be an independent display device such as a liquid crystal display, a television, etc. that is independent of the ultrasonic imaging device 10, or it may be a display screen on an electronic device such as a mobile phone or a tablet computer, etc.

[0026] In one embodiment of the present application, the memory 107 of the aforementioned ultrasonic imaging device 10 may be a flash memory card, a solid-state memory, a hard disk, etc.

[0027] In one embodiment of the present application, a computer-readable storage medium is also provided, which stores a plurality of program instructions. After the plurality of program instructions are called and executed by the processor 105, some or all of the steps or any combination of the steps in the ultrasound imaging method in each embodiment of the present application can be executed.

[0028] In one embodiment, the computer-readable storage medium may be the memory 107 , which may be a non-volatile storage medium such as a flash memory card, a solid-state memory, or a hard disk.

[0029] In one embodiment of the present application, the processor 105 of the aforementioned ultrasound imaging device 10 can be implemented by software, hardware, firmware, or a combination thereof, and can use circuits, single or multiple application-specific integrated circuits (ASICs), single or multiple general-purpose integrated circuits, single or multiple microprocessors, single or multiple programmable logic devices, or a combination of the aforementioned circuits or devices, or other suitable circuits or devices, so that the processor 105 can execute the corresponding steps of the ultrasound imaging method in each embodiment of the present application.

[0030] The following describes the method for evaluating the acquisition quality of ultrasound images of the present application in conjunction with the accompanying drawings.

[0031] Combine Figure 1 The schematic structural diagram of the ultrasonic imaging device 10 is shown in FIG. Figure 2 The method for evaluating the acquisition quality of an ultrasound image provided in an embodiment of the present application may include the following steps:

[0032] 201. Acquire an ultrasound image of a target tissue;

[0033] The ultrasonic imaging device 10 generally supports multiple modes of ultrasonic examination, such as B-mode, color Doppler mode, ultrasound elastography mode, power Doppler mode, and vector blood flow mode. The B-mode is used to obtain ultrasonic grayscale images of human tissue, the color Doppler mode can be used to obtain color Doppler images of human tissue. The color Doppler images, power Doppler mode, and vector blood flow mode are generally used to analyze blood flow in human tissue, and the ultrasound elastography mode is used to obtain elasticity images of human tissue. Elasticity images are generally used to analyze strain information of human tissue.

[0034] In an embodiment of the present application, an ultrasound image of a target tissue can be acquired. The ultrasound image can include an ultrasound grayscale image and a sampling image superimposed and displayed within a sampling frame of the ultrasound grayscale image. The sampling image can include a color Doppler image, an elasticity image, a power Doppler image, or a vector blood flow image. The target tissue can be part of the patient's body tissue to be examined, such as the thyroid gland, breast, and uterus. The sampling frame can be manually selected, for example, by manually selecting the sampling frame on the displayed ultrasound grayscale image of the tissue; the sampling frame can also be automatically called out by the machine and then manually adjusted to an appropriate position; or the sampling frame can be automatically called out by the machine and automatically adjusted to an appropriate position, which can be a lesion area.

[0035] In one possible implementation, refer to Figure 3 In step 201, the ultrasonic imaging device 10 may specifically perform the following steps:

[0036] 2011. Transmitting a first ultrasonic wave toward a target tissue and receiving an ultrasonic echo returned from the target tissue to obtain a first ultrasonic echo signal;

[0037] In the B mode, the ultrasonic imaging apparatus 10 may transmit ultrasonic waves (referred to as first ultrasonic waves) to the target tissue and receive ultrasonic echoes returned from the target tissue to obtain first ultrasonic echo signals.

[0038] 2012. Performing signal processing on the first ultrasonic echo signal to obtain an ultrasonic grayscale image;

[0039] The obtained first ultrasonic echo signal is subjected to beam synthesis, image processing and the like, so as to obtain an ultrasonic grayscale image, which can represent a B image of the target tissue.

[0040] 2013. Receive the operation instruction to switch to sampling mode:

[0041] The ultrasonic imaging device 10 can receive an input mode switching operation instruction and switch to the corresponding sampling mode. The sampling mode may include a color Doppler mode, an elasticity mode, an energy Doppler mode or a vector blood flow mode. For example, the ultrasonic imaging device 10 is generally provided with options corresponding to each mode. For example, the B mode generally corresponds to the option marked with "B", the color Doppler mode generally corresponds to the option marked with "C", the energy Doppler mode generally corresponds to the option marked with "P", and the elasticity mode generally corresponds to the option marked with "E". The input of the operation instruction can be achieved by key, touch, voice or gesture, which is not limited here.

[0042] 2014. In response to the operation instruction, display a sampling frame on the ultrasonic grayscale image;

[0043] The size and position of the sampling frame may be default, or may be adjusted by the user, or may be intelligently set by the ultrasound imaging device 10 through analysis of the ultrasound grayscale image (eg, analysis of the lesion).

[0044] 2015. Transmitting a second ultrasonic wave toward the target tissue and receiving an ultrasonic echo returned from the target tissue to obtain a second ultrasonic echo signal;

[0045] 2016. Performing signal processing on the second ultrasonic echo signal to obtain a sampled image that is superimposed and displayed within a sampling frame of the ultrasonic grayscale image;

[0046] The sampling image may be one or more of a color Doppler image, an elasticity image, a power Doppler image, and a vector blood flow image.

[0047] In a possible implementation, an ultrasonic image including an ultrasonic grayscale image and a sampling image superimposed and displayed in a sampling frame of the ultrasonic grayscale image may be read from a storage medium.

[0048] 202. Determine a lesion area in an ultrasound image;

[0049] After step 201 , the ultrasound imaging apparatus 10 may determine a lesion region in the ultrasound image.

[0050] In a possible implementation, after step 201 , the ultrasound imaging device 10 may determine a lesion area in the ultrasound image in response to receiving an instruction to save the ultrasound image.

[0051] In a possible implementation, the user may select a lesion area in the ultrasound image based on experience, and the ultrasound imaging device 10 may determine the lesion area in the ultrasound image based on the user's selection operation.

[0052] Alternatively, in a possible implementation, the ultrasound imaging device 10 may analyze the ultrasound image and automatically determine the lesion area in the ultrasound image.

[0053] Regarding the method of determining the lesion area, in one possible implementation, the lesion area of the ultrasound grayscale image can be determined, or, in one possible implementation, the lesion area of the sampling image can be determined, or, in one possible implementation, the lesion area can be determined based on the ultrasound grayscale image and the color Doppler image, or, in one possible implementation, the lesion area can be determined based on the ultrasound grayscale image and the elasticity image.

[0054] It should be noted that the determination of the above-mentioned lesion area can use the boundary segmentation algorithm or target detection algorithm in traditional image processing, or can use machine learning or deep learning algorithm. Taking breast lesions as an example, machine learning or deep learning algorithm is to put the image of the breast lesion boundary and the boundary or region of interest ROI frame coordinates that have been marked by the doctor into a deep learning segmentation or target detection network for training, such as a convolutional neural network. During the training process, the error between the predicted value and the calibrated position is calculated, and it is continuously iterated and gradually approximated to obtain a reference model for lesion segmentation or position detection. For breast images of different modes (such as color Doppler mode, elasticity mode, energy Doppler mode or vector blood flow mode), different models or algorithms can be selected to determine the lesion area.

[0055] 203. Determine the overlap between the lesion area and the sampling frame;

[0056] For example, the degree of overlap may be determined based on the ratio of the intersection area to the union area of the lesion area and the sampling frame, or based on the distance between the center of the lesion area and the center of the sampling frame.

[0057] 204. Determine an evaluation result of the acquisition quality of the ultrasound image according to the degree of overlap;

[0058] In one possible implementation, in step 204, the ultrasound imaging device 10 may determine the acquisition quality level or acquisition quality score of the ultrasound image based on the overlap determined in step 203, and then display the acquisition quality level or acquisition quality score of the ultrasound image.

[0059] The higher the overlap, the better the ultrasound image acquisition quality level or acquisition quality score. For example, a correspondence between overlap and acquisition quality level or acquisition quality score can be pre-set. For example, when the overlap is between 0 and 0.3, the acquisition quality level is poor; when the overlap is between 0.3 and 0.6, the acquisition quality level is good; and when the overlap is between 0.6 and 1, the acquisition quality level is excellent. Different acquisition quality levels or acquisition quality scores can be distinguished by different text, graphics, or colors.

[0060] In one possible implementation, whether the ultrasound image meets a preset condition (referred to as a first preset condition) can be determined based on the degree of overlap. For example, in step 204, when the degree of overlap is greater than or equal to a preset threshold, the ultrasound imaging device 10 can determine that the acquisition quality of the ultrasound image meets the first preset condition. When the degree of overlap is less than the preset threshold, it is determined that the acquisition quality of the ultrasound image does not meet the first preset condition. If the acquisition quality of the ultrasound image meets the first preset condition, it can be considered that the acquisition quality of the ultrasound image is qualified, or the acquisition quality meets the requirements, the acquisition quality reaches a preset level, the acquisition quality reaches a preset score, etc., and the ultrasound image has a high credibility as a basis for disease diagnosis, and the ultrasound image can be saved. If the acquisition quality of the ultrasound image does not meet the first preset condition, it can be considered that the acquisition quality of the ultrasound image is unqualified, or the acquisition quality is insufficient to meet the requirements, the acquisition quality does not reach a preset level, the acquisition quality does not reach a preset score, etc., and the credibility of the ultrasound image as a basis for disease diagnosis is low, and a rescan can be prompted.

[0061] In a possible implementation, the acquisition quality of the ultrasound image can be comprehensively evaluated in combination with the image quality of the ultrasound image. The evaluation method for the acquisition quality of the ultrasound image also includes:

[0062] Determining the image quality of ultrasound images;

[0063] Determining the evaluation result of the acquisition quality of the ultrasound image based on the overlap in step 204 further includes:

[0064] An evaluation result of the acquisition quality of the ultrasound image is determined based on the coincidence degree and the image quality of the ultrasound image.

[0065] In one possible implementation, the above-mentioned determination of the image quality of the ultrasound image includes determining the image quality of the ultrasound image based on at least one of the following: image grayscale, image clarity, the proportion of the effective area of the image, whether there are spots, snowflakes or mesh in the image, and the probe, probe parameters or imaging parameters used.

[0066] It should be noted that the acquisition quality of the ultrasound image can be comprehensively evaluated based on the aforementioned overlap and image quality. For example, when the overlap is between 0 and 0.3, the acquisition quality level is poor regardless of whether the image quality is high or low; when the overlap is between 0.3 and 0.6, the image quality is high and the acquisition quality level is good, while the image quality is low and the acquisition quality level is poor; when the overlap is between 0.6 and 1, the image quality is high and the acquisition quality level is excellent, while the image quality is low and the acquisition quality level is poor or good. For another example, when the overlap is greater than or equal to a preset threshold and the image quality is high, it can be determined that the acquisition quality of the ultrasound image meets the first preset condition; when the overlap is greater than or equal to the preset threshold and the image quality is low, it can be determined that the acquisition quality of the ultrasound image does not meet the first preset condition; and when the overlap is less than the preset threshold, it is determined that the acquisition quality of the ultrasound image does not meet the first preset condition.

[0067] Taking image grayscale as an example, the image grayscale can include the overall grayscale of the ultrasound image or the grayscale within the valid area. Image quality can be determined based on at least one of the following: whether the mean image grayscale is within a threshold range, whether the image grayscale is uniform, and whether the extreme grayscale value meets the grayscale extreme value standard. To determine whether the ultrasound image grayscale is uniform, a grayscale histogram can be plotted. By determining whether the grayscale distribution in the grayscale histogram is uniform, it can be ensured that the image grayscale is not concentrated in a specific area, thereby affecting the image quality.

[0068] If the grayscale of an ultrasound image meets grayscale standards, for example, if the grayscale mean of the ultrasound image is appropriate and the image is uniform, then the ultrasound image can more accurately display the morphology of the thyroid or breast, and the ultrasound image quality is high. Conversely, if the grayscale of the ultrasound image does not meet the grayscale standards, the ultrasound image quality is low. Therefore, the quality of the ultrasound image can be determined by the grayscale of the ultrasound image. For example, grayscale standards for ultrasound image quality such as grayscale mean, grayscale uniformity, and grayscale extremes can be set. Furthermore, the deviation between the grayscale of the ultrasound image and the grayscale standard can be calculated, and a functional relationship or other corresponding relationship between the deviation and image quality can be established to determine the quality of the ultrasound image based on the relationship between the grayscale of the ultrasound image and the grayscale standard. Of course, the deviation between the grayscale of the ultrasound image and the grayscale standard can be evaluated from a single perspective, such as the grayscale uniformity dimension, or it can be evaluated from multiple dimensions, such as the grayscale mean, grayscale extremes, and grayscale uniformity, to comprehensively obtain the deviation between the grayscale of the ultrasound image and the grayscale standard.

[0069] Taking image clarity as an example, if the clarity of an ultrasound image is high, the image quality is also high; if the clarity of an ultrasound image is low, the image quality is also low. Ultrasound image clarity can be a specific value, expressed as a score out of 10, a score out of 100, or a percentage; it can also be a qualitative standard, including clear, relatively clear, relatively blurry, or blurry. Image clarity can be calculated based on whether the ultrasound image is too bright or too dark, or whether the resolution of the ultrasound image is high enough.

[0070] In one embodiment, the clarity of the ultrasound image can be calculated based on the gradient information. Generally speaking, the higher the gradient value, the richer the edge information of the picture, and the clearer the image. For example, a functional relationship or other corresponding relationship between the gradient information of the effective area and the image clarity can be established. For example, the image clarity can be calculated based on the gradient information by using the Brenner gradient function, the Tenengrad gradient function, the Laplacian gradient function, etc. In another embodiment, the artificial intelligence model can be trained by inputting two types of thyroid or breast ultrasound images with clear and blurred images. For example, the artificial intelligence model can perform a binary classification problem of clarity and blur on the ultrasound image, and for the input ultrasound image to be tested, the artificial intelligence model can input a clear or blurred classification result. It should be emphasized that the artificial intelligence model can also grade the clarity of the ultrasound image into clear, relatively clear, relatively blurred, blurred, etc., so that the artificial intelligence model can output a clarity grade for the input ultrasound image to be tested.

[0071] Take the example of whether there are spots, snowflakes or reticulations in the image. To detect whether there are spots, snowflakes or reticulations in the ultrasound image, the entire ultrasound image can be detected; or the effective area can be determined in the ultrasound image first, and then the ultrasound image in the effective area can be detected. It can be understood that if there are spots, snowflakes or reticulations in the ultrasound image, the spots, snowflakes or reticulations in the ultrasound image may cover the key structures of the thyroid or breast, affecting the quality of the image. Therefore, a functional relationship or other corresponding relationship between the presence of spots, snowflakes or reticulations and the effectiveness of the image can be established. For example, the larger the range of spots, snowflakes or reticulations in the ultrasound image, the lower the quality of the image; conversely, the smaller the range of spots, snowflakes or reticulations in the ultrasound image, the higher the quality of the image; when there are no spots, snowflakes or reticulations in the ultrasound image, the image has the highest quality in the evaluation dimension of image defects. Furthermore, different weights can be assigned to the three image defects of spots, snowflakes or reticulation according to the different degrees of their influence on the identification of the thyroid or breast in the image, so as to determine the quality of the ultrasound image based on whether spots, snowflakes or reticulation exist in the detected ultrasound image.

[0072] Detection of speckles, snowflakes, or reticulation in ultrasound images can be performed by testing whether the texture of the ultrasound image meets preset image texture standards. For example, a pre-trained image texture detection model can be used to input the ultrasound image into the detection model to determine whether the texture meets the preset image texture standards. Image texture includes the presence of speckles, snowflakes, and reticulation.

[0073] Taking the effective area ratio of an image as an example, the quality of an ultrasound image can be determined by the effective area ratio of the ultrasound image. The effective area of an ultrasound image can be the area of the ultrasound image relevant to the acquisition of detection information. For example, for the thyroid gland, the effective area can be the area of the ultrasound image containing the thyroid image, or the image area of a thyroid nodule, or other areas of the ultrasound image relevant to the acquisition of detection information. The purpose of detecting the effective area ratio of an ultrasound image is to ensure that the effective area of the ultrasound image accounts for an appropriate proportion of the overall image. For example, the proportion should not be too small, but should be greater than 1 / 2. For example, a specific detection method is to obtain the effective area through image processing methods such as threshold segmentation, calculate the ratio of the effective area to the overall image area, and determine whether the ratio meets the preset ratio requirement. The size or ratio of the effective area is related to parameters such as the ultrasound scanning depth or the magnification / reduction factor. In one embodiment, the ultrasound scanning depth can be detected to determine whether it meets the standard, for example, whether the ultrasound scanning depth is within a threshold range, to determine whether the effective area ratio of the ultrasound image is appropriate.

[0074] It is understandable that if the effective area ratio of the ultrasound image is too small, it is difficult to accurately reflect the morphology of the thyroid or breast on the ultrasound image, which is not conducive to obtaining detection information based on the ultrasound image. Therefore, the quality of the ultrasound image can be determined by the effective area ratio of the ultrasound image. For example, the effective area ratio of the ultrasound image can be calculated, and a functional relationship or other corresponding relationship between the effective area ratio of the ultrasound image and the image quality can be established to determine the quality of the ultrasound image by the effective area ratio of the ultrasound image.

[0075] Taking the probe, probe parameters, and / or imaging parameters as an example, the quality of an ultrasound image can be determined by the correspondence between the probe, probe parameters, and / or imaging parameters and the thyroid or breast being examined, as included in the ultrasound image. When performing ultrasound examinations on patients, different probe parameters and imaging parameters need to be selected based on the different examination sites to achieve optimal imaging results for each examination site. For example, linear array probes are frequently used for superficial thyroid and breast examinations, while convex array probes are less frequently used for abdominal organs. However, in practice, due to inexperience or negligence, users may mistakenly use an ultrasound probe and corresponding probe parameters designed for the abdomen, as well as imaging parameters corresponding to the abdomen, during thyroid or breast ultrasound imaging. This can result in a poorly obtained high-quality thyroid or breast ultrasound image, impacting the image quality. Alternatively, users may mistakenly use imaging parameters designed for the breast, which can also result in a poorly obtained high-quality thyroid ultrasound image, impacting the image quality.

[0076] The tissue category contained in the ultrasound image can be identified and compared with the probe, probe parameters, and imaging parameters used to scan the ultrasound image. When the tissue category contained in the ultrasound image corresponds to the probe, probe parameters, and imaging parameters used, the quality of the ultrasound image is determined to be high. When the tissue category contained in the ultrasound image does not correspond to the probe, probe parameters, and imaging parameters used, the quality of the ultrasound image is determined to be low. The tissue category of the ultrasound image can be compared with the probe, probe parameters, and imaging parameters used to scan the ultrasound image, or the tissue category of the ultrasound image can be compared with one or both of the probe, probe parameters, and imaging parameters used to scan the ultrasound image to determine a corresponding relationship, thereby determining the quality of the image. Furthermore, a functional relationship or other corresponding relationship can be established between the corresponding relationship between the type of probe, probe parameter, and / or imaging parameter and the thyroid gland or breast to be measured included in the ultrasound image and the image quality, so as to determine the quality of the ultrasound image through this corresponding relationship.

[0077] When using the ultrasonic imaging device 10 to detect the target tissue of the patient, performing a quality assessment on the acquired ultrasonic image can help the auxiliary operator obtain a higher-quality ultrasonic image, thereby reducing the probability of misdiagnosis and the possibility of re-scanning the medical personnel.

[0078] Alternatively, in one possible implementation, the ultrasound image processing method provided herein may be applied not only to the ultrasound imaging device 10 but also to other computer devices other than the ultrasound imaging device 10 (referred to as target computer devices), such as laptop computers, tablet computers, and desktop computers. After the ultrasound imaging device 10 acquires an ultrasound image of the target tissue, it may transmit the image to the target computer device, which then stores the image in a storage medium. Step 201 may specifically include: the target computer device reads the ultrasound image from the storage medium.

[0079] refer to Figure 4 In a possible implementation, the method of the embodiment of the present application may further include the following steps:

[0080] 401. Acquire at least two frames of ultrasonic grayscale images of a target tissue;

[0081] 402. Determine a lesion area of at least two frames of ultrasound grayscale images;

[0082] 403. Determine the lesion level of the lesion area of at least two frames of ultrasound grayscale images respectively;

[0083] In one possible implementation, the lesion grade of the lesion area in at least two frames of ultrasound grayscale images can be determined based on the corresponding grade of the breast imaging report and data system BI-RADS. The lesion grade can indicate whether the lesion is benign or malignant. Based on the lesion grade, it can be determined whether the ultrasound image meets a preset condition (referred to as a second preset condition). For details, see steps 404 and 405.

[0084] 404. Based on the lesion levels of the lesion areas of the at least two frames of ultrasound grayscale images meeting a similar condition, it is indicated that the acquisition quality of the at least two frames of ultrasound grayscale images meets a second preset condition;

[0085] 405. Based on the fact that the lesion levels of the lesion areas of the at least two frames of ultrasound grayscale images do not meet the similarity condition, it is indicated that the acquisition quality of the at least two frames of ultrasound grayscale images does not meet the second preset condition;

[0086] If the acquisition quality of at least two frames of ultrasonic grayscale images meets the second preset condition, the reliability of the ultrasonic images as a basis for disease diagnosis can be considered high. Conversely, if the acquisition quality of at least two frames of ultrasonic grayscale images meets the second preset condition, the reliability of the ultrasonic images as a basis for disease diagnosis can be considered low. Whether the acquisition quality of at least two frames of ultrasonic grayscale images meets the second preset condition can be indicated using text, graphics, or other means. The second preset condition can refer to the description of the first preset condition above and will not be repeated here.

[0087] It should be noted that step 404 only limits the lesion level to a necessary condition that the acquisition quality of at least two frames of ultrasound grayscale images meets the second preset condition, and does not limit the lesion level to a necessary and sufficient condition that the acquisition quality of at least two frames of ultrasound grayscale images meets the second preset condition; step 405 is also subject to this explanation and will not be repeated here. It should be noted that the similarity condition can be met in that the lesion levels are identical or nearly identical. Here, nearly identical can be considered to be a small difference in lesion levels. For example, if the lesion levels differ by one level, it can be considered to be nearly identical. The failure to meet the similarity condition can be that the lesion levels are different or differ significantly. For example, if the lesion levels differ by two or more levels, it can be considered to be a significant difference.

[0088] In a possible implementation, the target tissue may include breast tissue, and the at least two frames of ultrasound grayscale images may include a cross-sectional image and a longitudinal section image of the breast tissue. Step 403 may specifically include: determining the lesion grade of the lesion area of the cross-sectional image and the lesion grade of the lesion area of the longitudinal section image according to the corresponding grade of the Breast Imaging Reporting and Data System BI-RADS. It should be noted that the cross-sectional image is generally also referred to as a cross-sectional image, and the longitudinal section image is generally also referred to as a longitudinal section image. The cross-sectional plane is generally a plane with the maximum diameter or close to the maximum diameter of the lesion, and the longitudinal section is a plane perpendicular or approximately perpendicular to the cross-sectional plane.

[0089] In one possible implementation, after step 401 and before step 404 and step 405, the acquisition quality of the ultrasound image may be further comprehensively evaluated in combination with the aforementioned image quality. For example, the resolution and / or clarity of at least two frames of ultrasound grayscale images may be determined separately, and whether the resolution and / or clarity meet a third preset condition (such as a preset resolution and / or clarity) may be used as a judgment condition for whether the acquisition quality of at least two frames of ultrasound grayscale images meets a second preset condition. For example, step 404 may be specifically, if the resolution and / or clarity meet the third preset condition, and the lesion level meets the similarity condition, it is indicated that the acquisition quality of at least two frames of ultrasound grayscale images meets the second preset condition. Step 405 may be specifically, if the resolution and / or clarity do not meet the third preset condition, or the lesion level does not meet the similarity condition, it is indicated that the acquisition quality of at least two frames of ultrasound grayscale images does not meet the second preset condition. The third preset condition may be that the resolution and / or clarity are greater than a certain threshold. The similarity condition is understood with reference to the above description and will not be repeated here.

[0090] In a possible implementation, the ultrasound grayscale image and the sampling image may not be displayed superimposed, but as two independent images. Figure 5 The method of the embodiment of the present application may further include the following steps:

[0091] 501. Acquire at least one frame of ultrasonic grayscale image and at least one frame of sampling image of a target tissue;

[0092] The sampling images include color Doppler images, elasticity images, power Doppler images or vector blood flow images;

[0093] 502. Determine a lesion area of at least one frame of ultrasound grayscale image and a lesion area of at least one frame of sampling image;

[0094] 503. Determine the lesion level of at least one frame of ultrasound grayscale image and the lesion level of the lesion area of at least one frame of sampling image;

[0095] 504. Based on the lesion level of the lesion area of the at least one frame of ultrasonic grayscale image and the lesion level of the lesion area of the at least one frame of sampling image meeting a similar condition, it is indicated that the acquisition quality of the at least one frame of ultrasonic grayscale image and the at least one frame of sampling image meets a second preset condition.

[0096] 505. Based on the fact that the lesion level of the lesion area in the at least one ultrasonic grayscale image frame and the lesion level of the lesion area in the at least one sampled image frame do not meet a similarity condition, it is indicated that the acquisition quality of the at least one ultrasonic grayscale image frame and the at least one sampled image frame does not meet a second preset condition. The similarity condition is understood with reference to the above description and is not further elaborated here.

[0097] It is understandable that this embodiment can further combine the aforementioned image quality to comprehensively evaluate the acquisition quality of the ultrasound image. The relevant content can be understood with reference to the aforementioned embodiment and will not be repeated here.

[0098] In one possible implementation, Figure 5 The target tissue in the corresponding embodiment may include breast tissue, and step 503 may specifically include: determining the lesion level of at least one frame of ultrasound grayscale image and the lesion level of the lesion area of at least one frame of sampling image according to the corresponding grades of the breast imaging report and data system BI-RADS.

[0099] The above describes in detail the ultrasound image acquisition quality assessment method provided by this application. This application also provides an ultrasound image acquisition quality assessment device. Figure 6 The ultrasound image acquisition quality assessment device of the present application may be a computer device, including a processor 601 and a storage medium 602. In one possible implementation, the two may be connected via a bus. The storage medium 602 stores computer instructions. By calling the computer instructions, the processor 601 is configured to execute the following steps:

[0100] Acquire an ultrasonic image of the target tissue, the ultrasonic image including an ultrasonic grayscale image and a sampling image superimposed and displayed within a sampling frame of the ultrasonic grayscale image, the sampling image including a color Doppler image, an elasticity image, a power Doppler image, or a vector blood flow image;

[0101] determining the lesion area in ultrasound images;

[0102] Determine the overlap between the lesion area and the sampling frame;

[0103] The evaluation result of the acquisition quality of the ultrasound image is determined based on the overlap.

[0104] In a possible implementation, the processor 601 is specifically configured to perform the following steps:

[0105] The ultrasound image is read from the storage medium.

[0106] In a possible implementation, the processor 601 is specifically configured to perform the following steps:

[0107] transmitting a first ultrasonic wave to a target tissue, and receiving an ultrasonic echo returned from the target tissue to obtain a first ultrasonic echo signal;

[0108] performing signal processing on the first ultrasonic echo signal to obtain an ultrasonic grayscale image;

[0109] Receive an operation instruction to switch to a sampling mode, which includes a color Doppler mode, elasticity mode, power Doppler mode, or vector blood flow mode:

[0110] In response to the operation instruction, displaying a sampling frame on the ultrasonic grayscale image;

[0111] transmitting a second ultrasonic wave toward the target tissue, and receiving an ultrasonic echo returned from the target tissue to obtain a second ultrasonic echo signal;

[0112] Signal processing is performed on the second ultrasonic echo signal to obtain a sampling image that is superimposed and displayed within a sampling frame of the ultrasonic grayscale image.

[0113] In a possible implementation, the processor 601 is specifically configured to perform the following steps:

[0114] receiving an instruction to save the ultrasound image;

[0115] In response to the save instruction, a lesion region in the ultrasound image is determined.

[0116] In a possible implementation, the processor 601 is specifically configured to perform the following steps:

[0117] Determine the lesion area of the ultrasound grayscale image, and / or determine the lesion area of the sampling image.

[0118] In a possible implementation, the processor 601 is specifically configured to perform the following steps:

[0119] Determining an acquisition quality level or an acquisition quality score of the ultrasound image according to the degree of overlap;

[0120] Displays the acquisition quality level or acquisition quality score of the ultrasound image.

[0121] In a possible implementation, the processor 601 is specifically configured to perform the following steps:

[0122] When the overlap is greater than or equal to a preset threshold, determining that the acquisition quality of the ultrasound image meets a first preset condition;

[0123] When the degree of overlap is less than a preset threshold, it is determined that the acquisition quality of the ultrasound image does not meet the first preset condition.

[0124] In a possible implementation, the processor 601 is specifically configured to perform the following steps:

[0125] saving the ultrasound image based on that the acquisition quality of the ultrasound image meets a first preset condition;

[0126] If the acquisition quality of the ultrasound image does not meet the first preset condition, a prompt is given to rescan the image.

[0127] In a possible implementation, the processor 601 is specifically configured to perform the following steps:

[0128] acquiring at least two frames of ultrasonic grayscale images of the target tissue;

[0129] determining a lesion area of at least two frames of ultrasound grayscale images;

[0130] determining the lesion level of the lesion area of at least two frames of ultrasound grayscale images respectively;

[0131] Based on the lesion levels of the lesion areas of the at least two frames of ultrasound grayscale images meeting a similar condition, it is suggested that the acquisition quality of the at least two frames of ultrasound grayscale images meets a second preset condition;

[0132] Based on the fact that the lesion levels of the lesion areas of the at least two frames of ultrasound grayscale images do not meet the similarity condition, it is suggested that the acquisition quality of the at least two frames of ultrasound grayscale images does not meet the second preset condition.

[0133] In a possible implementation, the target tissue includes breast tissue, and the at least two frames of ultrasound grayscale images include a cross-sectional image and a longitudinal cross-sectional image of the breast tissue;

[0134] The processor 601 is specifically configured to perform the following steps:

[0135] The lesion grade of the lesion area in the cross-sectional image and the lesion grade of the lesion area in the longitudinal section image were determined according to the corresponding grades of the Breast Imaging Reporting and Data System BI-RADS.

[0136] In a possible implementation, the processor 601 is specifically configured to perform the following steps:

[0137] determining whether the resolution and / or clarity of at least two frames of ultrasound grayscale images meet a third preset condition;

[0138] Based on the resolution and / or clarity satisfying the third preset condition and the lesion level meeting the similarity condition, it is prompted that the acquisition quality of at least two frames of ultrasound grayscale images meets the second preset condition;

[0139] Based on the fact that the resolution and / or clarity do not meet the third preset condition, or the lesion level does not meet the similarity condition, it is prompted that the acquisition quality of at least two frames of ultrasound grayscale images does not meet the second preset condition.

[0140] In a possible implementation, the processor 601 is specifically configured to perform the following steps:

[0141] Acquire at least one frame of ultrasonic grayscale image and at least one frame of sampling image of target tissue, where the sampling image includes a color Doppler image, an elasticity image, a power Doppler image, or a vector blood flow image;

[0142] determining a lesion area of at least one frame of ultrasound grayscale image and a lesion area of at least one frame of sampling image;

[0143] determining a lesion level of at least one frame of ultrasound grayscale image and a lesion level of a lesion region of at least one frame of sampling image;

[0144] Based on the lesion level of the lesion area of the at least one frame of ultrasound grayscale image and the lesion level of the lesion area of the at least one frame of sampling image meeting a similar condition, it is suggested that the acquisition quality of the at least one frame of ultrasound grayscale image and the at least one frame of sampling image meets a second preset condition;

[0145] Based on the fact that the lesion level of the lesion area of at least one frame of ultrasound grayscale image and the lesion level of the lesion area of at least one frame of sampling image do not meet the similarity condition, it is suggested that the acquisition quality of at least one frame of ultrasound grayscale image and at least one frame of sampling image does not meet the second preset condition.

[0146] In a possible implementation, the target tissue includes breast tissue, and the processor 601 is specifically configured to perform the following steps:

[0147] The lesion grade of at least one frame of ultrasound grayscale image and the lesion grade of the lesion area of at least one frame of sampling image are determined respectively according to the corresponding grades of the breast imaging report and data system BI-RADS.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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.

[0149] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0150] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0151] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0152] It should be noted that in actual applications, the target tissue can be human, animal, etc. The target tissue can be the face, spine, heart, uterus, thyroid, or pelvic floor, etc., or other parts of the human body, such as the brain, bones, liver, or kidney, etc., and this application does not limit this.

[0153] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for evaluating the acquisition quality of an ultrasound image, characterized in that: include: Acquire an ultrasonic image of the target tissue, the ultrasonic image including an ultrasonic grayscale image and a sampling image superimposed and displayed within a sampling frame of the ultrasonic grayscale image, the sampling image including a color Doppler image, an elasticity image, a power Doppler image, or a vector blood flow image; determining a lesion area in the ultrasound image; Determining the degree of overlap between the lesion area and the sampling frame, comprising: determining the degree of overlap based on a ratio of an intersection area to a union area between the lesion area and the sampling frame, or determining the degree of overlap based on a distance between a center of the lesion area and a center of the sampling frame; An evaluation result of the acquisition quality of the ultrasound image is determined according to the overlap.

2. The method according to claim 1, characterized in that The acquiring of an ultrasonic image of the target tissue comprises: The ultrasound image is read from a storage medium.

3. The method according to claim 1, characterized in that The acquiring of an ultrasonic image of the target tissue comprises: transmitting a first ultrasonic wave toward the target tissue, and receiving an ultrasonic echo returned from the target tissue to obtain a first ultrasonic echo signal; performing signal processing on the first ultrasonic echo signal to obtain the ultrasonic grayscale image; Receive an operation instruction to switch to a sampling mode, wherein the sampling mode includes a color Doppler mode, an elasticity mode, a power Doppler mode, or a vector blood flow mode: In response to the operation instruction, displaying the sampling frame on the ultrasonic grayscale image; transmitting a second ultrasonic wave toward the target tissue, and receiving an ultrasonic echo returned from the target tissue to obtain a second ultrasonic echo signal; Signal processing is performed on the second ultrasonic echo signal to obtain the sampling image that is superimposed and displayed within a sampling frame of the ultrasonic grayscale image.

4. The method according to claim 1, wherein Determining the lesion area in the ultrasound image includes: receiving a storage instruction for the ultrasound image; In response to the saving instruction, a lesion area in the ultrasound image is determined.

5. The method according to claim 1, wherein Determining the lesion area in the ultrasound image includes: Determine the lesion area of the ultrasound grayscale image, and / or determine the lesion area of the sampling image.

6. The method according to claim 1, characterized in that The determining of the evaluation result of the acquisition quality of the ultrasound image according to the overlap includes: determining an acquisition quality level or an acquisition quality score of the ultrasound image according to the overlap; The acquisition quality level or acquisition quality score of the ultrasound image is displayed.

7. The method according to claim 1, characterized in that The determining of the evaluation result of the acquisition quality of the ultrasound image according to the overlap includes: When the overlap is greater than or equal to a preset threshold, determining that the acquisition quality of the ultrasound image meets a first preset condition; When the overlap is less than a preset threshold, it is determined that the acquisition quality of the ultrasound image does not meet a first preset condition.

8. The method according to claim 7, characterized in that The method further comprises: saving the ultrasound image based on that the acquisition quality of the ultrasound image meets a first preset condition; Based on the fact that the acquisition quality of the ultrasound image does not meet the first preset condition, a prompt is given to rescan the image.

9. The method according to any one of claims 1 to 8, characterized in that The method further comprises: Acquiring at least two frames of ultrasonic grayscale images of the target tissue; determining a lesion area of the at least two frames of ultrasound grayscale images; Determining the lesion grade of the lesion area of the at least two frames of ultrasound grayscale images according to the corresponding grade of the breast imaging report and data system BI-RADS; Based on the lesion levels of the lesion areas of the at least two frames of ultrasonic grayscale images meeting a similarity condition, indicating that the acquisition quality of the at least two frames of ultrasonic grayscale images meets a second preset condition, wherein meeting the similarity condition indicates that the lesion levels are the same or nearly the same; Based on the fact that the lesion levels of the lesion areas of the at least two frames of ultrasonic grayscale images do not meet the similarity condition, it is suggested that the acquisition quality of the at least two frames of ultrasonic grayscale images does not meet the second preset condition.

10. The method according to claim 9, characterized in that The target tissue includes breast tissue, and the at least two frames of ultrasound grayscale images include a cross-sectional image and a longitudinal cross-sectional image of the breast tissue; The step of determining the lesion grade of the lesion area of the at least two frames of ultrasound grayscale images according to the grades corresponding to the Breast Imaging Reporting and Data System (BI-RADS) comprises: The lesion grade of the lesion area in the cross-sectional image and the lesion grade of the lesion area in the longitudinal section image are determined according to the corresponding grades of the Breast Imaging Reporting and Data System (BI-RADS).

11. The method according to claim 9, characterized in that The method further comprises: determining whether the resolution and / or clarity of the at least two frames of ultrasonic grayscale images meet a third preset condition; Based on the resolution and / or clarity satisfying the third preset condition, and the lesion level meeting the similarity condition, it is indicated that the acquisition quality of the at least two frames of ultrasound grayscale images meets the second preset condition; Based on the fact that the resolution and / or clarity does not satisfy the third preset condition, or the lesion level does not satisfy the similarity condition, it is suggested that the acquisition quality of the at least two frames of ultrasound grayscale images does not satisfy the second preset condition.

12. The method according to any one of claims 1 to 8, characterized in that The method further comprises: Acquire at least one frame of ultrasonic grayscale image and at least one frame of sampling image of the target tissue, wherein the sampling image includes a color Doppler image, an elasticity image, a power Doppler image, or a vector blood flow image; Determining the lesion area of the at least one frame of ultrasonic grayscale image and the lesion area of the at least one frame of sampling image; Determining the lesion level of the at least one frame of ultrasonic grayscale image and the lesion level of the lesion area of the at least one frame of sampling image; Based on the lesion level of the lesion area of the at least one frame of ultrasonic grayscale image and the lesion level of the lesion area of the at least one frame of sampling image meeting a similar condition, it is suggested that the acquisition quality of the at least one frame of ultrasonic grayscale image and the at least one frame of sampling image meets a second preset condition; Based on the fact that the lesion level of the lesion area of the at least one frame of ultrasound grayscale image and the lesion level of the lesion area of the at least one frame of sampling image do not meet the similarity condition, it is suggested that the acquisition quality of the at least one frame of ultrasound grayscale image and the at least one frame of sampling image does not meet the second preset condition.

13. The method according to claim 12, characterized in that The target tissue includes breast tissue, and determining the lesion level of the at least one frame of ultrasound grayscale image and the lesion level of the lesion area of the at least one frame of sampling image includes: The lesion grade of the at least one frame of ultrasound grayscale image and the lesion grade of the lesion area of the at least one frame of sampling image are determined respectively according to the corresponding grades of the Breast Imaging Reporting and Data System BI-RADS.

14. The method according to any one of claims 1 to 8, characterized in that The method further comprises: determining an image quality of the ultrasound image; The evaluation result of determining the acquisition quality of the ultrasound image according to the overlap includes: An evaluation result of the acquisition quality of the ultrasound image is determined according to the overlap degree and the image quality of the ultrasound image.

15. The method according to claim 14, characterized in that Determining the image quality of the ultrasound image includes determining the image quality of the ultrasound image based on at least one of the following: image grayscale, image clarity, image effective area ratio, whether there are spots, snowflakes or mesh in the image, and the probe, probe parameters or imaging parameters used.

16. An ultrasonic imaging device, characterized in that: include: Probe; a transmitting circuit, wherein the transmitting circuit excites the probe to transmit ultrasonic waves toward the target tissue; a receiving circuit, wherein the receiving circuit controls the probe to receive the ultrasonic echo returned from the target tissue to obtain an ultrasonic echo signal; a processor, wherein the processor processes the ultrasonic echo signal to obtain an ultrasonic image of the target tissue; a display, wherein the display displays the ultrasound image; The processor is configured to execute the steps of the method according to any one of claims 1 to 15.

Images