Image enhancement method, device and electronic equipment for ultrasound imaging

Abstract

The present invention relates to the field of image enhancement technology, and specifically to an image enhancement method, device and electronic device for ultrasound contrast imaging. The present invention analyzes the grayscale of each pixel point at each position in each grayscale uniform window in different video frames, and the corresponding frame sequence distribution, to obtain the overall tissue beating cycle; obtain the periodic video frame in the ultrasound contrast imaging video; use the arterial phase contrast imaging video or the portal phase contrast imaging video as the video to be analyzed, and obtain the overall lesion area possibility of the pixel point at each position according to the grayscale change degree of the pixel point at each position in the corresponding video to be analyzed, and the instantaneousness of the grayscale change; obtain the enhanced grayscale value of the pixel point at each position on each video frame, obtain the enhanced video frame, and obtain the enhanced ultrasound contrast imaging video. The present invention improves the effect of image enhancement by obtaining the accurate grayscale value of each pixel point in each video frame.

Description

Technical Field

[0001] The present invention relates to the technical field of image enhancement, and in particular to an image enhancement method, device and electronic equipment for ultrasonic angiography. Background Art

[0002] Ultrasound contrast imaging is a safe, effective, real-time dynamic imaging examination technology. Ultrasound contrast agents are injected into the human body through intravenous injection. The contrast agents are distributed to various tissues and organs throughout the body through blood circulation. By observing the perfusion and distribution of contrast agents in tissues in real time and recording ultrasound images at different times, the patient's condition can be analyzed and observed more quickly and accurately.

[0003] In the prior art, linear enhancement is usually used to enhance the lesion area in the post-contrast image. However, due to the injection amount of the contrast agent, the contrast agent concentration is too low or too high, which may not be able to fully display the grayscale difference between the lesion area and the surrounding tissue, making the grayscale performance of some lesion areas and surrounding tissue areas similar, resulting in poor image enhancement effect. Summary of the invention

[0004] In order to solve the technical problem that the grayscale performance of some lesion areas and surrounding tissue areas may be similar due to the influence of the injection amount of contrast agent, resulting in poor image enhancement effect, the purpose of the present invention is to provide an image enhancement method, device and electronic equipment for ultrasound contrast imaging, and the technical solutions adopted are as follows:

[0005] The present invention provides an image enhancement method for ultrasound contrast imaging, the method comprising:

[0006] Acquire an ultrasound contrast-enhanced video of a liver patient, wherein the ultrasound contrast-enhanced video includes an arterial phase contrast-enhanced video and a portal venous phase contrast-enhanced video;

[0007] Constructing a sliding window to traverse the initial video frame of the ultrasound contrast imaging video, and screening out a grayscale uniform window on the initial video frame according to the grayscale distribution of the pixel points between different preset neighborhood ranges of each sliding window; obtaining the local tissue beating cycle of each grayscale uniform window and the overall tissue beating cycle according to the grayscale difference between the initial video frame and each other video frame at each position of the pixel points in each grayscale uniform window, and the corresponding frame sequence distribution; obtaining the periodic video frame in the ultrasound contrast imaging video based on the overall tissue beating cycle;

[0008] Taking the arterial phase angiography video or the portal phase angiography video as the video to be analyzed, according to the grayscale distribution of the pixel point at each position in each video to be analyzed in each periodic video frame and the corresponding frame sequence distribution, the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position in the corresponding video to be analyzed are obtained; according to the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position in different videos to be analyzed, the overall lesion area possibility of the pixel point at each position is obtained;

[0009] According to the initial grayscale value of the pixel point at each position on each video frame and the overall lesion area possibility, the enhanced grayscale value of the pixel point at each position on each video frame is obtained, the enhanced video frame is obtained, and the enhanced ultrasound contrast video is obtained.

[0010] Furthermore, the method for obtaining the grayscale uniform window includes:

[0011] For any sliding window, the density uniformity of the corresponding sliding window at each gray level is obtained according to the difference in the number of pixels at each gray level between different preset neighborhood ranges. The difference in the number of pixels is negatively correlated with the density uniformity.

[0012] The mean value of the density uniformity of each sliding window at all gray levels is obtained as the gray uniformity of each sliding window; the first preset number of sliding windows with the highest gray uniformity values ​​of all sliding windows are selected, and the corresponding sliding windows are used as gray uniformity windows.

[0013] Furthermore, the method for acquiring the local tissue beating cycle includes:

[0014] Obtaining the average of the grayscale differences between the initial video frame and each other video frame for the pixels at different positions within each grayscale uniform window as the grayscale difference between the initial video frame and each other video frame for each grayscale uniform window;

[0015] Selecting the first preset index number of grayscale difference values ​​of each grayscale uniform window between the initial video frame and different other video frames with the smallest grayscale difference value, and using the corresponding other video frames as reference period video frames of each grayscale uniform window;

[0016] The mean difference of the corresponding frame sequences between all adjacent reference period video frames in each gray uniform window is obtained as the local tissue beating period of each gray uniform window.

[0017] Furthermore, the method for acquiring the whole tissue beating cycle includes:

[0018] The mean value of the grayscale difference between the initial video frame and all reference period video frames of each grayscale uniform window is obtained and normalized as the period credibility;

[0019] The local tissue beating cycle is weighted according to the cycle credibility of each gray-scale uniform window to obtain the local weighted beating cycle of each gray-scale uniform window;

[0020] The mean of the local weighted beating periods of all grayscale uniform windows is obtained as the overall tissue beating period.

[0021] Furthermore, the method for obtaining the grayscale change degree includes:

[0022] For any video to be analyzed, the difference in grayscale value between each periodic video frame and the next periodic video frame of the pixel point at each position is obtained as the relative grayscale difference corresponding to each periodic video frame;

[0023] Perform negative correlation normalization mapping on the corresponding frame sequence of each periodic video frame in the video to be analyzed as the grayscale difference credibility of each periodic video frame;

[0024] According to the relative grayscale difference and grayscale difference credibility corresponding to the pixel point at each position in the video to be analyzed in each periodic video frame, the grayscale change degree of the pixel point at each position in the video to be analyzed is obtained. The relative grayscale difference and grayscale difference credibility are positively correlated with the grayscale change degree.

[0025] Furthermore, the method for obtaining the instantaneousness of the grayscale change includes:

[0026] For any video to be analyzed, the possibility of drastic changes in the pixel points at each position within different preset video frame ranges is obtained according to the fluctuation degree of the grayscale value of the pixel point at each position within different preset video frame ranges and the corresponding frame sequence of the central video frame within the corresponding video frame range. The fluctuation degree and the corresponding frame sequence of the central video frame are both positively correlated with the possibility of drastic changes.

[0027] The pixel point at each position within different preset video frame ranges with the smallest probability of drastic change is selected, and the corresponding frame sequence of the central video frame within the corresponding video frame range is negatively correlated as the instantaneous grayscale change of the corresponding video to be analyzed.

[0028] Furthermore, the method for obtaining the overall lesion area possibility includes:

[0029] For any video to be analyzed, the ratio of the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position is obtained as the possibility of the local lesion area;

[0030] The sum of the local lesion area probabilities of all videos to be analyzed is obtained and normalized and mapped as the overall lesion area probability of the pixel point at each position.

[0031] Furthermore, the initial grayscale value and the overall lesion area possibility are both positively correlated with the enhanced grayscale value.

[0032] The present invention also proposes an image enhancement device for ultrasound contrast imaging, comprising a video acquisition module, a cycle determination module, a lesion analysis module and a video enhancement module:

[0033] Video acquisition module: acquires ultrasound contrast-enhanced video of liver patients, including arterial phase contrast-enhanced video and portal phase contrast-enhanced video;

[0034] The cycle determination module: constructs a sliding window to traverse the initial video frame of the ultrasound contrast imaging video, and selects a grayscale uniform window on the initial video frame according to the grayscale distribution of the pixel points between different preset neighborhood ranges of each sliding window; obtains the local tissue beating cycle of each grayscale uniform window according to the grayscale difference between the initial video frame and each other video frame at each position of the pixel point in each grayscale uniform window, and obtains the overall tissue beating cycle; based on the overall tissue beating cycle, obtains the periodic video frame in the ultrasound contrast imaging video;

[0035] Lesion analysis module: taking the arterial phase angiography video or the portal phase angiography video as the video to be analyzed, obtaining the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position in each video to be analyzed according to the grayscale distribution of the pixel point at each position in each periodic video frame and the corresponding frame sequence distribution; obtaining the overall lesion area possibility of the pixel point at each position according to the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position in different videos to be analyzed;

[0036] Video enhancement module: according to the initial gray value of the pixel point at each position on each video frame and the overall lesion area possibility, the enhanced gray value of the pixel point at each position on each video frame is obtained, the enhanced video frame is obtained, and the enhanced ultrasound contrast video is obtained.

[0037] The present invention also proposes an electronic device, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the image enhancement method for ultrasound angiography as described above are implemented.

[0038] The present invention has the following beneficial effects:

[0039] The present invention takes into account that the grayscale of the lesion area will change after the contrast agent is added, analyzes the grayscale of the pixel point at each position in each grayscale uniform window in different video frames, and the corresponding frame sequence distribution, obtains the overall tissue beating cycle, and can accurately capture the beating cycle of the tissue; based on the overall tissue beating cycle, obtains the periodic video frame in the ultrasound contrast video, and the periodic video frame analysis provides a time reference, ensures the accuracy and consistency of the analysis, and helps to reduce redundant information in the analysis process; takes the arterial phase contrast video or the portal phase contrast video as the video to be analyzed, and obtains the overall lesion area possibility of the pixel point at each position according to the grayscale change degree of the pixel point at each position in the corresponding video to be analyzed, and the instantaneousness of the grayscale change, and can quantify a lesion area possibility value for the pixel point at each position; according to the initial grayscale value and lesion area possibility of the pixel point at each position on each video frame, obtains the enhanced grayscale value of the pixel point at each position on each video frame, obtains the enhanced video frame, and obtains the enhanced ultrasound contrast video, which more clearly displays the lesion area and provides a more intuitive visual effect, so that doctors can more easily identify and analyze. The present invention improves the effect of image enhancement by obtaining the accurate grayscale value of each pixel in each video frame. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A flowchart of an image enhancement method for ultrasound contrast imaging provided by one embodiment of the present invention;

[0042] Figure 2 A flow chart of a method for acquiring a local tissue beating cycle provided by an embodiment of the present invention;

[0043] Figure 3 A flow chart of a method for obtaining a grayscale change degree provided by an embodiment of the present invention;

[0044] Figure 4 A structural block diagram of an image enhancement device for ultrasound angiography provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the image enhancement method, device and electronic device for ultrasound contrast imaging proposed by the present invention, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0046] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0047] The following is a detailed description of a specific scheme of an image enhancement method, device and electronic equipment for ultrasound contrast imaging provided by the present invention in conjunction with the accompanying drawings.

[0048] See also Figure 1 , which shows a flow chart of an image enhancement method for ultrasound contrast imaging provided by an embodiment of the present invention, the specific method includes:

[0049] Step S1: Acquire an ultrasound contrast-enhanced video of a liver patient, where the ultrasound contrast-enhanced video includes an arterial phase contrast-enhanced video and a portal venous phase contrast-enhanced video.

[0050] In an embodiment of the present invention, for liver patients, since the tumor tissue is mainly supplied by the hepatic artery, and has a large blood flow and rich blood vessels, the contrast agent will be distributed to various tissues and organs throughout the body with the blood circulation. By observing the perfusion and distribution of the contrast agent in the tissue in real time and recording ultrasound images at different times, the patient's condition can be analyzed considering that the lesion area reacts more to the contrast agent and the grayscale changes more. First, the ultrasound contrast video of the liver patient is obtained, and the ultrasound contrast video includes the arterial phase contrast video and the portal phase contrast video.

[0051] The arterial phase and portal phase are important periods for observing liver lesions in medical imaging examinations. After the contrast agent is injected, it first enters the arterial system. During the arterial phase, the concentration of contrast agent in the blood vessels is relatively high, so the blood vessels appear bright on the image. As the contrast agent flows from the arteries through the capillary network into the venous system, the venous phase appears later than the arterial phase. Although the concentration of contrast agent in the blood vessels is still high during the venous phase, it is lower than that in the arterial phase, so the brightness of the blood vessels on the image may be relatively low.

[0052] It should be noted that, in one embodiment of the present invention, the method for acquiring arterial phase angiography video and portal phase angiography video includes: if the grayscale values ​​of pixels at different positions on the previous video frame are greater than the grayscale values ​​on the subsequent video frame, the corresponding video frame is used as the staging point of the ultrasound angiography video, the range before the staging point is the arterial phase angiography video, and the range after the staging point is the portal phase angiography video.

[0053] Step S2: construct a sliding window to traverse the initial video frame of the ultrasound contrast imaging video, and screen out the grayscale uniform window on the initial video frame according to the grayscale distribution of pixels between different preset neighborhood ranges in each sliding window; obtain the local tissue beating cycle of each grayscale uniform window and the overall tissue beating cycle according to the grayscale difference between the initial video frame and each other video frame at each position in each grayscale uniform window, and the corresponding frame sequence characteristics; based on the overall tissue beating cycle, obtain the periodic video frame in the ultrasound contrast imaging video.

[0054] In order to process a large amount of video information more efficiently and maintain sensitivity to detail features, a sliding window is constructed to traverse the initial video frame of the ultrasound contrast imaging video. It should be noted that in one embodiment of the present invention, the size of the sliding window is 9×9; in other embodiments of the present invention, the size of the sliding window can be set according to specific circumstances, which is not limited or elaborated here.

[0055] Normal tissues usually have relatively stable physiological functions and structures. The injection of contrast agents will not cause obvious abnormal changes, and the grayscale performance is relatively uniform. However, the grayscale changes in the lesion area are large. By analyzing the grayscale distribution of pixels between different preset neighborhood ranges, the grayscale performance in different neighborhood ranges can be reflected. The more uniform the grayscale distribution, the more conducive it is to capturing key information in the video. According to the grayscale distribution of pixels between different preset neighborhood ranges in each sliding window, the grayscale uniform window on the initial video frame is screened out.

[0056] Preferably, in one embodiment of the present invention, the method for obtaining the gray uniformity window includes:

[0057] For any sliding window, the density uniformity of the corresponding sliding window at each gray level is obtained according to the difference in the number of pixels at each gray level between different preset neighborhood ranges. The difference in the number of pixels is negatively correlated with the density uniformity.

[0058] In one embodiment of the present invention, for any sliding window, the formula for density uniformity is expressed as:

[0059] ;

[0060] in, Indicates that the sliding window is in gray level Density uniformity; Indicates that the sliding window is The gray level of the neighborhood The number of pixels below; Indicates that the sliding window is The number of pixels in grayscale within a neighborhood; Indicates the number of neighborhood ranges within the sliding window.

[0061] In the formula for density uniformity, Indicates that the sliding window is The first The gray level between the neighborhoods The greater the difference in the number of pixels, the less similar the pixel density distribution is under the corresponding gray level, and the more uneven the gray distribution is. It means calculating the mean of the difference in the number of pixels at each gray level between all preset neighborhood ranges, reflecting the overall distribution of each gray level within the sliding window. The smaller the mean, the more uniform the pixel density distribution and the greater the density uniformity.

[0062] The mean value of the density uniformity of each sliding window at all gray levels is obtained as the gray uniformity of each sliding window; the first preset number of sliding windows with the highest gray uniformity values ​​of all sliding windows are selected, and the corresponding sliding windows are used as gray uniformity windows.

[0063] It should be noted that, in one embodiment of the present invention, the grayscale level of the pixel is set to 16, the grayscale range in the image is 0-255, and the grayscale value is divided into 16 equal parts to obtain the grayscale value range of each grayscale level.

[0064] It should be noted that, in one embodiment of the present invention, the method for obtaining the preset neighborhood range is, for any sliding window, dividing the sliding window into three equal parts in the horizontal direction and the vertical direction respectively, to constitute 6 different neighborhood ranges of the sliding window; the preset number is 4. In other embodiments of the present invention, the neighborhood range and the preset number can be set according to the specific situation, and are not limited or elaborated here.

[0065] After a beating cycle, the grayscale of the pixel in the video frame is consistent with that in the initial video frame. By analyzing the grayscale difference, the change of the image content of the pixel at each position can be reflected; by analyzing the change of the grayscale difference with the frame sequence, it can be revealed whether the image change shows regular fluctuations. The more regular the fluctuation, the more obvious the periodic phenomenon. According to the grayscale difference between the initial video frame and each other video frame of the pixel at each position in each grayscale uniform window, and the corresponding frame sequence distribution, the local tissue beating cycle of each grayscale uniform window is obtained.

[0066] Preferably, in one embodiment of the present invention, the method for obtaining the local tissue beating cycle is described in Figure 2 , which shows a flow chart of a method for acquiring a local tissue beating cycle, including:

[0067] Step S201: obtaining the mean value of the grayscale difference between the initial video frame and each other video frame of the pixel points at different positions in each grayscale uniform window as the grayscale difference degree between the initial video frame and each other video frame of each grayscale uniform window.

[0068] In one embodiment of the present invention, the grayscale difference degree is expressed as:

[0069] ;

[0070] in, Indicates The grayscale uniform window is located between the initial video frame and the The grayscale difference between other video frames; Indicates The gray value of the pixel point at the position in the initial video frame; Indicates The pixel point at the position Grayscale values ​​in other video frames; Indicates The number of pixels in a uniform grayscale window.

[0071] In the formula for grayscale difference, It means calculating the mean grayscale difference between the initial video frame and each other video frame of the pixels at different positions in each grayscale uniform window, that is, the grayscale difference degree. The greater the grayscale difference between the corresponding video frames, the greater the grayscale difference degree.

[0072] Step S202: selecting a preset index number of grayscale difference values ​​between the initial video frame and other different video frames of each grayscale uniform window with the smallest grayscale difference values, and using the corresponding other video frames as periodic video frames of each grayscale uniform window.

[0073] Since the beating of tissues is periodic, the grayscale of pixels between the initial video frame and the final video frame of a cycle is consistent. Therefore, the one with the smallest grayscale difference is more likely to be the subsequent periodic video frame of the grayscale uniform window.

[0074] It should be noted that, in one embodiment of the present invention, the size of the preset indicator is 10; in other embodiments of the present invention, the size of the preset number can be set according to specific circumstances, and is not limited or elaborated here.

[0075] Step S203: obtaining the mean difference of the corresponding frame sequences between all adjacent periodic video frames in each grayscale uniform window as the local tissue beat period of each grayscale uniform window.

[0076] The periodicity of video frame changes within each grayscale uniform window is quantified by taking the average value.

[0077] By analyzing the local tissue beating cycle within the grayscale uniform window, the dynamic changes of different positions can be understood more accurately. By comprehensively analyzing the local beating cycle within each grayscale uniform window, the beating cycle of the entire tissue can be obtained, and the dynamic characteristics of the tissue can be understood more comprehensively.

[0078] Preferably, in one embodiment of the present invention, the method for acquiring the whole tissue beating cycle includes:

[0079] The mean value of the grayscale difference between the initial video frame and all periodic video frames of each grayscale uniform window is obtained and normalized as the periodic credibility;

[0080] The local tissue beating cycle is weighted according to the cycle credibility of each grayscale uniform window to obtain the local weighted beating cycle of each grayscale uniform window; the average of the local weighted beating cycles of all grayscale uniform windows is obtained as the overall tissue beating cycle.

[0081] In one embodiment of the present invention, the formula for the overall tissue beating cycle is expressed as:

[0082] ;

[0083] in, Represents the overall tissue beating cycle; Indicates The grayscale uniform window is located between the initial video frame and the The grayscale difference between the video frames of each cycle; Indicates the number of periodic video frames; Indicates The local tissue beating cycle of a uniform grayscale window; Indicates the number of grayscale uniform windows; Represents the normalization function.

[0084] In the formula for the overall tissue beating cycle, It represents the product of the calculated cycle credibility and the local tissue beating cycle, that is, the local tissue beating cycle is weighted by the cycle credibility of each grayscale uniform window; Indicates the calculation The mean of the grayscale difference between the initial video frame and all the periodic video frames in a grayscale uniform window is normalized, that is, the cycle credibility. The larger the mean of the grayscale difference, the greater the grayscale difference, the more uneven the grayscale distribution, the less credible the local tissue beating cycle, and the smaller the cycle credibility, which makes the proportion of local tissue beating cycle smaller; the more credible the local tissue beating cycle, the greater the cycle credibility, which makes the proportion of local tissue beating cycle larger, the larger the local tissue beating cycle, and the larger the overall tissue beating cycle.

[0085] Based on the overall tissue beating cycle, the periodic video frames corresponding to the initial video frames are obtained. The representative periodic video frames are selected from the ultrasound contrast video to reflect the state of the tissue in different physiological cycles.

[0086] It should be noted that, starting from the initial video frame, corresponding video frames are obtained in sequence at intervals of the overall tissue beating cycle as periodic video frames to obtain periodic video frames in the ultrasound contrast imaging video.

[0087] Step S3: Use the arterial phase angiography video or the portal phase angiography video as the video to be analyzed, and obtain the grayscale change degree and grayscale change instantaneity of the pixel point at each position in each video to be analyzed according to the grayscale distribution of the pixel point at each position in each periodic video frame and the corresponding frame sequence distribution; obtain the overall lesion area possibility of the pixel point at each position according to the grayscale change degree and grayscale change instantaneity of the pixel point at each position in different videos to be analyzed.

[0088] The arterial phase and portal phase have the characteristics of "fast in and fast out". In the corresponding period, the lesion area is characterized by the larger grayscale difference of the pixel in the adjacent cycle video frames, and the time required for the change is very short, and the stable state can be reached quickly. Therefore, by analyzing the grayscale distribution of the pixel at each position in each cycle video frame, and the corresponding frame sequence distribution reflects the difference in grayscale changes between different layers of the pixel, that is, reflects the grayscale situation of the pixel changing over time, which helps to quantify the degree of grayscale change and the instantaneousness of grayscale change. Therefore, the arterial phase angiography video or portal phase angiography video is used as the video to be analyzed, and according to the grayscale distribution of the pixel at each position in each cycle video frame and the corresponding frame sequence distribution, the grayscale change degree of the pixel at each position in the corresponding video to be analyzed and the instantaneousness of grayscale change are obtained.

[0089] Preferably, in one embodiment of the present invention, the method for obtaining the grayscale change degree is as follows: Figure 3 , which shows a flow chart of a method for obtaining the grayscale change degree, including:

[0090] Step S301: For any video to be analyzed, the difference in grayscale value of the pixel point at each position between each periodic video frame and the next periodic video frame is obtained as the relative grayscale difference corresponding to each periodic video frame.

[0091] Dynamic changes in videos usually lead to changes in pixel grayscale values. By calculating the grayscale value difference of each pixel between adjacent video frames, dynamic changes can be captured. The greater the grayscale value difference, the greater the relative grayscale difference.

[0092] Step S302: performing negative correlation normalization mapping on the corresponding frame sequence of each periodic video frame in the video to be analyzed as the grayscale difference credibility of each periodic video frame.

[0093] Since the grayscale change of pixels in the lesion area is the largest at the beginning and gradually stabilizes over time, the corresponding frame sequence of each periodic video frame in the video to be analyzed is analyzed to reflect the credibility of the grayscale change of pixels in the lesion area. The larger the corresponding frame sequence, the lower the credibility of the grayscale change caused by the lesion.

[0094] Step S303: According to the relative grayscale difference and grayscale difference credibility corresponding to the pixel point at each position in the video to be analyzed in each periodic video frame, the grayscale change degree of the pixel point at each position in the video to be analyzed is obtained, and the relative grayscale difference and the grayscale difference credibility are both positively correlated with the grayscale change degree.

[0095] Among them, the greater the relative grayscale difference, that is, the greater the difference in grayscale values ​​between adjacent periodic video frames, the greater the grayscale change; the greater the grayscale difference credibility, the smaller the corresponding frame sequence of the periodic video frame, the more likely it is that the grayscale difference change of the pixel points in the lesion area, making the grayscale change greater, all of which are positively correlated.

[0096] In one embodiment of the present invention, the formula for grayscale change degree is expressed as:

[0097] ;

[0098] in, Indicates The video to be analyzed The probability of local lesion area of ​​pixel points at each position; Indicates The number of periodic video frames in the video to be analyzed; Indicates The periodic video frames correspond to the frame sequence in the video to be analyzed; Indicates The pixel point at the position Grayscale value on a periodic video frame; Indicates The pixel point at the position Grayscale value on a periodic video frame; Represents the normalization function.

[0099] In the formula for grayscale change, Indicates the calculation The pixel point at the position cycle video frames and the The difference in grayscale values ​​between the video frames of the first cycle, i.e. The relative grayscale difference of the video frames in each cycle. The greater the grayscale value difference, the greater the relative grayscale difference and the greater the grayscale change. Indicates The periodic video frames are negatively correlated with the corresponding frame sequence in the video to be analyzed. The larger the frame sequence, the lower the reference for the overall grayscale change and the smaller the credibility of the grayscale difference. Indicates the calculation The product of the relative grayscale difference and the grayscale difference credibility of the periodic video frames is calculated, and the product results of all periodic video frames are averaged to obtain the grayscale change degree of the pixel point at each position in the video to be analyzed. The greater the relative grayscale difference, the greater the grayscale difference credibility, and the greater the grayscale change degree.

[0100] Preferably, in one embodiment of the present invention, the method for obtaining the instantaneousness of grayscale change includes:

[0101] For any video to be analyzed, the possibility of drastic changes of the pixel points at each position within different preset video frame ranges is obtained according to the fluctuation degree of the grayscale value of the pixel point at each position within different preset video frame ranges and the corresponding frame sequence of the central video frame of the corresponding video frame range. The fluctuation degree and the corresponding frame sequence of the central video frame are positively correlated with the possibility of drastic changes.

[0102] The pixel point with the smallest drastic change probability value at each position in different preset video frame ranges is selected, and the corresponding frame sequence of the central video frame in the corresponding video frame range is negatively correlated as the instantaneous grayscale change of the corresponding video to be analyzed. Among them, the larger the corresponding frame sequence of the central video frame in the video to be analyzed, the longer it takes to enter the stable period, and the smaller the instantaneous grayscale change.

[0103] It should be noted that, in one embodiment of the present invention, the degree of fluctuation can be characterized by calculating the variance. The larger the variance, the greater the degree of fluctuation, the more unstable the gray value, and the more drastic the change. In other embodiments of the present invention, the degree of fluctuation can also be characterized by calculating the standard deviation. The specific means are well known to those skilled in the art and will not be described in detail here.

[0104] It should be noted that, in one embodiment of the present invention, the preset video frame range is to sequentially group every 5 periodic video frames in the video to be analyzed into a range to obtain different preset video frame ranges; in other embodiments of the present invention, the preset video frame range can be set according to the specific situation, which is not limited or elaborated here.

[0105] According to the grayscale change degree and grayscale change instantaneity of the pixel point at each position in the different videos to be analyzed, the overall lesion area possibility of the pixel point at each position is obtained.

[0106] Preferably, in one embodiment of the present invention, the method for obtaining the overall lesion area possibility includes:

[0107] For any video to be analyzed, the ratio of the grayscale change degree and the instantaneous grayscale change of the pixel point at each position is obtained as the possibility of the local lesion area;

[0108] The sum of the local lesion area probabilities of all videos to be analyzed is obtained and normalized and mapped as the overall lesion area probability of the pixel point at each position.

[0109] In one embodiment of the present invention, the formula for the overall lesion area likelihood is expressed as:

[0110] ;

[0111] ;

[0112] in, Indicates The overall lesion area probability of the pixel points at the position; Indicates The video to be analyzed The probability of local lesion area of ​​pixel points at each position; Indicates the first The probability of local lesion area of ​​pixel points at each position; Indicates the first The probability of local lesion area of ​​pixel points at each position; Indicates The video to be analyzed The grayscale change degree of the pixel at each position; Indicates The video to be analyzed The instantaneous grayscale change of the pixel at each position; Represents the normalization function.

[0113] In the formula for the overall lesion area likelihood, Indicates the calculation The video to be analyzed The product of the grayscale change degree and the instantaneous grayscale change of the pixel point at each position. The larger the product, the greater the possibility of the local lesion area and the greater the possibility of the overall lesion area.

[0114] Step S4: According to the initial grayscale value of the pixel point at each position on each video frame and the possibility of the lesion area, the enhanced grayscale value of the pixel point at each position on each video frame is obtained, the enhanced video frame is obtained, and the enhanced ultrasound contrast video is obtained.

[0115] In ultrasound contrast imaging, the grayscale value of the lesion area is often different from that of normal tissue. By enhancing the grayscale value of the lesion area, these differences can be made more obvious. Targeted adjustments and improvements are made based on the initial state of each frame of the image. The initial grayscale value is appropriately adjusted according to the possibility of the lesion area to enhance certain features of the image and improve the overall quality of the image. After obtaining the enhanced grayscale value of each pixel in each video frame, the enhancement operation of the video frame image is completed, which effectively improves the accuracy of the results of subsequent image processing. Therefore, according to the initial grayscale value and lesion area possibility of the pixel point at each position on each video frame, the enhanced grayscale value of the pixel point at each position on each video frame is obtained, the enhanced video frame is obtained, and the enhanced ultrasound contrast imaging video is obtained.

[0116] It should be noted that, in one embodiment of the present invention, both the initial gray value and the overall lesion area possibility are positively correlated with the enhanced gray value.

[0117] In one embodiment of the present invention, the formula for enhancing the gray value is expressed as:

[0118] ;

[0119] in, Indicates The enhanced gray value of the pixel point at each position in each video frame; Indicates The initial grayscale value of the pixel at each position in each video frame; Indicates The overall lesion area probability of the pixel point at the position.

[0120] In the formula for enhancing grayscale value, Indicates The product of the initial grayscale value of the pixel point at each position in each video frame and the overall lesion area possibility; It represents the sum of the calculated product result and the initial gray value, that is, the enhanced gray value of each pixel in each video frame. The larger the initial gray value, the greater the possibility of the overall lesion area and the larger the enhanced gray value.

[0121] After obtaining the enhanced grayscale value of each pixel in each frame of the image, the enhancement operation of the video frame image is completed, which effectively improves the accuracy of the subsequent image processing results and helps to observe and analyze the lesion condition.

[0122] In summary, the present invention analyzes the grayscale of each pixel point at each position in each grayscale uniform window in different video frames, and the corresponding frame sequence distribution, to obtain the overall tissue beating cycle; obtain the periodic video frame in the ultrasound contrast video; take the arterial phase contrast video or portal phase contrast video as the video to be analyzed, and obtain the grayscale change degree and instantaneousness of the grayscale change of the pixel point at each position in each video to be analyzed according to the grayscale distribution of each pixel point at each position in each periodic video frame, and the corresponding frame sequence distribution; and then obtain the overall lesion area possibility of the pixel point at each position; obtain the enhanced grayscale value of each pixel point at each position on each video frame, obtain the enhanced video frame, and obtain the enhanced ultrasound contrast video. The present invention improves the effect of image enhancement by obtaining the accurate grayscale value of each pixel point in each video frame.

[0123] Based on the same application concept as the image enhancement method for ultrasound contrast imaging provided in the embodiment of the present application, the present embodiment also proposes an image enhancement device for ultrasound contrast imaging, such as Figure 4 As shown, it includes: a video acquisition module 401, a cycle determination module 402, a lesion analysis module 403 and a video enhancement module 404:

[0124] Video acquisition module 401: acquiring ultrasound contrast imaging video of a liver patient, where the ultrasound contrast imaging video includes arterial phase contrast imaging video and portal venous phase contrast imaging video;

[0125] The cycle determination module 402: constructs a sliding window to traverse the initial video frame of the ultrasound contrast imaging video, and selects a grayscale uniform window on the initial video frame according to the grayscale distribution of the pixel points between different preset neighborhood ranges of each sliding window; obtains the local tissue beating cycle of each grayscale uniform window according to the grayscale difference between the initial video frame and each other video frame at each position of the pixel point in each grayscale uniform window, and obtains the overall tissue beating cycle; based on the overall tissue beating cycle, obtains the periodic video frame in the ultrasound contrast imaging video;

[0126] Lesion analysis module 403: taking the arterial phase angiography video or the portal phase angiography video as the video to be analyzed, obtaining the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position in each video to be analyzed according to the grayscale distribution of the pixel point at each position in each periodic video frame and the corresponding frame sequence distribution; obtaining the overall lesion area possibility of the pixel point at each position according to the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position in different videos to be analyzed;

[0127] Video enhancement module 404: according to the initial gray value of the pixel point at each position on each video frame and the overall lesion area possibility, obtain the enhanced gray value of the pixel point at each position on each video frame, obtain the enhanced video frame, and obtain the enhanced ultrasound contrast video.

[0128] It should be understood that the image enhancement device for ultrasound contrast imaging provided in this embodiment is used to execute the above-mentioned image enhancement method for ultrasound contrast imaging, and therefore has the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0129] The present invention also provides an electronic device, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the image enhancement method for ultrasound angiography as described above are implemented.

[0130] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0131] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An image enhancement method for ultrasound contrast imaging, characterized in that: The method comprises: Acquire an ultrasound contrast-enhanced video of a liver patient, wherein the ultrasound contrast-enhanced video includes an arterial phase contrast-enhanced video and a portal venous phase contrast-enhanced video; Constructing a sliding window to traverse the initial video frame of the ultrasound contrast imaging video, and screening out a grayscale uniform window on the initial video frame according to the grayscale distribution of the pixel points between different preset neighborhood ranges of each sliding window; obtaining the local tissue beating cycle of each grayscale uniform window and the overall tissue beating cycle according to the grayscale difference between the initial video frame and each other video frame at each position of the pixel points in each grayscale uniform window, and the corresponding frame sequence distribution; obtaining the periodic video frame in the ultrasound contrast imaging video based on the overall tissue beating cycle; Taking the arterial phase angiography video or the portal phase angiography video as the video to be analyzed, according to the grayscale distribution of the pixel point at each position in each video to be analyzed in each periodic video frame and the corresponding frame sequence distribution, the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position in the corresponding video to be analyzed are obtained; according to the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position in different videos to be analyzed, the overall lesion area possibility of the pixel point at each position is obtained; According to the initial grayscale value of the pixel point at each position on each video frame and the overall lesion area possibility, the enhanced grayscale value of the pixel point at each position on each video frame is obtained, the enhanced video frame is obtained, and the enhanced ultrasound contrast video is obtained; The method for obtaining the grayscale change degree includes: For any video to be analyzed, the difference in grayscale value between each periodic video frame and the next periodic video frame of the pixel point at each position is obtained as the relative grayscale difference corresponding to each periodic video frame; Perform negative correlation normalization mapping on the corresponding frame sequence of each periodic video frame in the video to be analyzed as the grayscale difference credibility of each periodic video frame; According to the relative grayscale difference and grayscale difference credibility corresponding to each pixel point at each position in the video to be analyzed in each periodic video frame, the grayscale change degree of the pixel point at each position in the video to be analyzed is obtained, and the relative grayscale difference and grayscale difference credibility are both positively correlated with the grayscale change degree; The method for obtaining the instantaneousness of the grayscale change includes: For any video to be analyzed, the possibility of drastic changes in the pixel points at each position within different preset video frame ranges is obtained according to the fluctuation degree of the grayscale value of the pixel point at each position within different preset video frame ranges and the corresponding frame sequence of the central video frame within the corresponding video frame range. The fluctuation degree and the corresponding frame sequence of the central video frame are both positively correlated with the possibility of drastic changes. The pixel point at each position within different preset video frame ranges with the smallest probability of drastic change is selected, and the corresponding frame sequence of the central video frame within the corresponding video frame range is negatively correlated as the instantaneous grayscale change of the corresponding video to be analyzed.

2. The image enhancement method for ultrasound contrast imaging according to claim 1, characterized in that: The method for obtaining the grayscale uniform window includes: For any sliding window, the density uniformity of the corresponding sliding window at each gray level is obtained according to the difference in the number of pixels at each gray level between different preset neighborhood ranges. The difference in the number of pixels is negatively correlated with the density uniformity. The mean value of the density uniformity of each sliding window at all gray levels is obtained as the gray uniformity of each sliding window; the first preset number of sliding windows with the highest gray uniformity values ​​of all sliding windows are selected, and the corresponding sliding windows are used as gray uniformity windows.

3. The image enhancement method for ultrasound contrast imaging according to claim 1, characterized in that: The method for acquiring the local tissue beating cycle includes: Obtaining the average of the grayscale differences between the initial video frame and each other video frame for the pixels at different positions within each grayscale uniform window as the grayscale difference between the initial video frame and each other video frame for each grayscale uniform window; Selecting the first preset index number of grayscale difference values ​​of each grayscale uniform window between the initial video frame and different other video frames with the smallest grayscale difference value, and using the corresponding other video frames as reference period video frames of each grayscale uniform window; The mean difference of the corresponding frame sequences between all adjacent reference period video frames in each gray uniform window is obtained as the local tissue beating period of each gray uniform window.

4. The image enhancement method for ultrasound contrast imaging according to claim 3, characterized in that: The method for obtaining the whole tissue beating cycle includes: The mean value of the grayscale difference between the initial video frame and all reference period video frames of each grayscale uniform window is obtained and normalized as the period credibility; The local tissue beating cycle is weighted according to the cycle credibility of each gray-scale uniform window to obtain the local weighted beating cycle of each gray-scale uniform window; The mean of the local weighted beating periods of all grayscale uniform windows is obtained as the overall tissue beating period.

5. The image enhancement method for ultrasound contrast imaging according to claim 1, characterized in that: The method for obtaining the overall lesion area possibility includes: For any video to be analyzed, the ratio of the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position is obtained as the possibility of the local lesion area; The sum of the local lesion area probabilities of all videos to be analyzed is obtained and normalized and mapped as the overall lesion area probability of the pixel point at each position.

6. The image enhancement method for ultrasound contrast imaging according to claim 1, characterized in that: The initial gray value and the overall lesion area possibility are both positively correlated with the enhanced gray value.

7. An image enhancement device for ultrasound contrast imaging, characterized in that: It includes video acquisition module, cycle determination module, lesion analysis module and video enhancement module: Video acquisition module: acquires ultrasound contrast-enhanced video of liver patients, including arterial phase contrast-enhanced video and portal phase contrast-enhanced video; The cycle determination module: constructs a sliding window to traverse the initial video frame of the ultrasound contrast imaging video, and selects a grayscale uniform window on the initial video frame according to the grayscale distribution of the pixel points between different preset neighborhood ranges of each sliding window; obtains the local tissue beating cycle of each grayscale uniform window according to the grayscale difference between the initial video frame and each other video frame at each position of the pixel point in each grayscale uniform window, and obtains the overall tissue beating cycle; based on the overall tissue beating cycle, obtains the periodic video frame in the ultrasound contrast imaging video; Lesion analysis module: taking the arterial phase angiography video or the portal phase angiography video as the video to be analyzed, according to the grayscale distribution of the pixel point at each position in each video to be analyzed in each periodic video frame and the corresponding frame sequence distribution, the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position in the video to be analyzed are obtained; According to the grayscale change degree and the instantaneousness of the grayscale change of the pixel point at each position in the different videos to be analyzed, the overall lesion area possibility of the pixel point at each position is obtained; The method for obtaining the grayscale change degree includes: For any video to be analyzed, the difference in grayscale value between each periodic video frame and the next periodic video frame of the pixel point at each position is obtained as the relative grayscale difference corresponding to each periodic video frame; Perform negative correlation normalization mapping on the corresponding frame sequence of each periodic video frame in the video to be analyzed as the grayscale difference credibility of each periodic video frame; According to the relative grayscale difference and grayscale difference credibility corresponding to each pixel point at each position in the video to be analyzed in each periodic video frame, the grayscale change degree of the pixel point at each position in the video to be analyzed is obtained, and the relative grayscale difference and grayscale difference credibility are both positively correlated with the grayscale change degree; The method for obtaining the instantaneousness of the grayscale change includes: For any video to be analyzed, the possibility of drastic changes in the pixel points at each position within different preset video frame ranges is obtained according to the fluctuation degree of the grayscale value of the pixel point at each position within different preset video frame ranges and the corresponding frame sequence of the central video frame within the corresponding video frame range. The fluctuation degree and the corresponding frame sequence of the central video frame are both positively correlated with the possibility of drastic changes. The pixel point at each position within different preset video frame ranges with the smallest probability of drastic change is selected, and the corresponding frame sequence of the central video frame within the corresponding video frame range is negatively correlated as the instantaneous grayscale change of the corresponding video to be analyzed; Video enhancement module: According to the initial grayscale value of the pixel point at each position on each video frame and the possibility of the overall lesion area, the enhanced grayscale value of the pixel point at each position on each video frame is obtained, the enhanced video frame is obtained, and the enhanced ultrasound contrast video is obtained.

8. An electronic device, characterized in that: The electronic device stores a program or instruction, and when the program or instruction is executed by the processor, the steps of an image enhancement method for ultrasound angiography as described in any one of claims 1 to 7 are implemented.

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